Patent Publication Number: US-7899208-B2

Title: Image processing device and method, recording medium, and program for tracking a desired point in a moving image

Description:
TECHNICAL FIELD 
     The present invention relates to an image processing apparatus and method, a recording medium, and a program, and, in particular, to an image processing apparatus and method, a recording medium, and a program capable of reliably tracking a desired point in a moving image varying from time to time. 
     BACKGROUND ART 
     A variety of methods for automatically tracking a desired point in a moving image have been proposed. 
     For example, Patent Document 1 proposes technology in which tracking is performed using a motion vector related to a block corresponding to an object to be tracked. 
     Patent Document 2 proposes technology in which a region related to an object to be tracked is estimated and the region is tracked on the basis of the estimation result of the motion of the region. 
     [Patent Document 1] Japanese Unexamined Patent Application Publication No. 6-143235 
     [Patent Document 2] Japanese Unexamined Patent Application Publication No. 5-304065 
     DISCLOSURE OF INVENTION 
     Problems to be Solved by the Invention 
     However, in the technology described in Patent Document 1, tracking is performed using only one motion vector. Accordingly, sufficient robust performance is not provided. In addition, when the object to be tracked disappears from user&#39;s sight due to, for example, the rotation of an image containing the object, and subsequently, the tracking point appears again, the tracking point cannot be tracked any more, which is a problem. 
     In the technology described in Patent Document 2, a region is utilized. Thus, the robust performance is increased. However, when the region is too large in order to increase the robust performance and when, for example, the image of a face of a child captured by a home video recorder is tracked and zoomed in, the body of the child, which has a larger area than the face, is sometimes tracked and zoomed in. 
     Additionally, in the both technologies, if occlusion occurs (i.e., if the object to be tracked is temporarily covered by another object) or the object to be tracked temporarily disappears due to, for example, a scene change, a robust tracking is difficult. 
     Accordingly, it is an object of the present invention to provide reliable tracking of the tracking point even when an object is rotated, occlusion occurs, or a scene change occurs. 
     Means for Solving the Problems 
     According to the present invention, an image processing apparatus includes position estimating means for estimating the position of a second point representing a tracking point in an image of a temporally next unit of processing, the second point corresponding to a first point representing the tracking point in an image of a temporally previous unit of processing, generating means for generating estimated points serving as candidates of the first point when the position of the second point is inestimable, determining means for determining the second point in the next unit of processing on the basis of the estimation result of the position estimating means when the position of the second point in the next unit of processing is estimable, and selecting means for selecting the first point from among the estimated points when the position of the second point is inestimable. 
     The unit of processing can be a frame. 
     The position estimating means can further compute the accuracy of the estimation of the position and, if the computed accuracy is greater than a reference value, the position estimating means determines that the position of the second point is estimable. 
     If the position of the second point in the next unit of processing is inestimable, the position estimating means can estimate the position of the second point on the basis of the first point selected by the selecting means. 
     If the position of the second point is estimable, the position estimating means can consider the position of the second point to be a new first point and can estimate the position of the tracking point in the image of the next unit of processing. 
     The generating means can include region estimating means for estimating a set of at least one point, the set belonging to an object including the first point, to be a target region in the previous unit of processing or in a more previous unit of processing than the previous unit of processing and estimated point generating means for generating the estimated point on the basis of the target region. 
     The region estimating means can find a position that overlaps at least the target region serving as an object to be estimated by prediction, can determine a region estimation range at the predicted point including the tracking point in the unit of processing for estimating the target region, can set sample points in the determined region estimation range, and can estimate a region consisting of a set of the sample points having the same motion and having the largest dimensions among the sample points to be the target region. 
     The shape of the region estimation range can be fixed. 
     The shape of the region estimation range can be variable. 
     The region estimating means can estimate the target region in a more previous unit of processing than the previous unit of processing, and the generating means can generate a point in the estimated target region in the more previous unit of processing than the previous unit of processing as the estimated point. 
     The region estimating means can estimate the target region in the previous unit of processing, and the generating means can generate a point forming the target region as the estimated point. 
     The region estimating means can estimate points that are adjacent to the first point and that have pixel values similar to the pixel value of the first point and points that are adjacent to the points adjacent to the first point to be the target region. 
     The region estimating means can extract sample points in a region having a predetermined size and including the first point in a more previous unit of processing than the previous unit of processing, and the region estimating means can estimate a region including the points in the previous unit of processing obtained by shifting a region of the sample points having the same motion and having the largest dimensions by an amount of the same motion to be the target region. 
     The image processing apparatus can further include template generating means for generating a template and correlation computing means for computing a correlation between a block representing a predetermined region in the next unit of processing and a block representing a predetermined region of the template in a unit of processing more previous than the unit of processing of the block by one or more units of processing when the second point is not determined on the basis of the estimated point. The tracking point can be detected by using at least the determining means when the correlation is determined to be high on the basis of the correlation computed by the correlation computing means. 
     The template generating means can determine a predetermined region around the tracking point to be the template. 
     The template generating means can generate the template on the basis of the target region. 
     When the correlation is determined to be high on the basis of the correlation computed by the correlation computing means, the second point can be determined on the basis of a relationship between the block representing the predetermined region of the template in a unit of processing more previous than a block representing the predetermined region in the next unit of processing by one or more units of processing and the tracking point and on the basis of the position of the block having the correlation determined to be high. 
     The template generating means can determine a region formed from a sample point in the target region and a predetermined area around the sample point to be the template. 
     the correlation computing means can determine the correlation by computing an error between the block in the next unit of processing and a block of the template in a unit of processing more previous than the unit of processing of the block by one or more units of processing. 
     The image processing apparatus can further include detecting means for detecting a scene change. The position estimating means and the selecting means terminate the processes thereof on the basis of a predetermined condition and change the condition on the basis of the presence of the scene change when the position estimating means and the selecting means are unable to select the second point from among the estimated points. 
     The determining means can further include evaluation value computing means for computing an evaluation value representing a correlation between pixels of interest representing at least one pixel including the first point in the temporally previous unit of processing and the corresponding pixels representing at least one pixel in the temporally next unit of processing and defined on the basis of a motion vector of the pixels of interest, variable value computing means for computing a variable value representing the variation of a pixel value with respect to the pixels of interest, and accuracy computing means for computing the accuracy of the motion vector. 
     The number of the pixels of interest can be equal to the number of the corresponding pixels. 
     The variable value can be a value for indicating the variation of a pixel value in the spatial direction. 
     The variable value can indicate one of a degree of dispersion and a dynamic range. 
     The unit of processing can be one of a frame and a field. 
     The accuracy computing means can compute the accuracy of the motion vector on the basis of a value normalized from the evaluation value with respect to the variable value. 
     The accuracy computing means can determine a value normalized from the evaluation value with respect to the variable value to be the accuracy of the motion vector when the variable value is greater than a predetermined threshold value, and the accuracy computing means can determine a fixed value indicating that the accuracy of the motion vector is low when the variable value is less than the predetermined threshold value. 
     The evaluation value computing means can compute the evaluation value representing the sum of absolute differences between pixels in a block including the pixels of interest and pixels in a block including the corresponding pixels. 
     The variable value computing means can compute the variable value representing the sum of values obtained by dividing the sum of absolute differences between the pixels of interest and the adjacent pixels that are adjacent to the pixels of interest in a block including the pixels of interest by the number of the adjacent pixels. 
     The accuracy computing means can include comparing means for comparing the variable value with a first reference value, difference computing means for computing the difference between a second reference value and the value normalized from the evaluation value with respect to the variable value, and outputting means for computing the accuracy of the motion vector on the basis of the comparison result of the comparing means and the difference computed by the difference computing means and outputting the accuracy of the motion vector. 
     The image processing apparatus can further include motion vector detecting means for detecting the motion vector from an input image and delivering the motion vector to the evaluation value computing means, motion compensating means for motion-compensating the input image on the basis of the motion vector detected by the motion vector detecting means, selecting means for selecting one of the image that is motion-compensated by the motion compensating means and the image that is not motion-compensated on the basis of the accuracy of the motion vector, and encoding means for encoding the image selected by the selecting means. 
     The image processing apparatus can further include frequency distribution computing means for computing a frequency distribution weighted with the accuracy of the motion vector and maximum value detecting means for detecting a maximum value of the frequency distribution computed by the frequency distribution computing means and detecting a background motion on the basis of the detected maximum value. 
     The image processing apparatus can further include average value computing means for computing the average of the accuracy of the motion vectors in the unit of processing and determining means for comparing the average computed by the average value computing means with a reference value and determining the presence of a scene change on the basis of the comparison result. 
     The average value computing means can compute one average for one unit of processing. 
     The image processing apparatus can further include first-point detecting means for detecting the first point of a moving object in an image, correction area setting means for setting a correction area having a predetermined size around the object in the image on the basis of the estimation result, correcting means for correcting the image in the correction area in the image, and display control means for controlling the display of the image including the image in the correction area corrected by the correcting means. 
     The correcting means can correct blurring of the image. 
     The correcting means can include delivering means for delivering a control signal for identifying an image in the correction area and a parameter indicating the level of blurring of the image, feature detecting means for detecting the feature of the image in the correction area identified on the basis of the control signal and outputting a feature code representing the detected feature, storage means for storing the parameter representing the level of blurring of the image and a coefficient corresponding to the feature code output from the feature detecting means, readout means for reading out the parameter and the coefficient corresponding to the feature code output from the feature detecting means from the storage means, inner-product computing means for computing the inner product of the values of pixels in the input image on the basis of the coefficient read out by the readout means, and selectively-outputting means for selecting one of the computation result from the inner-product computing means and the value of the pixel of the input image and outputting the selected one. The image in the correction area can be corrected so that blurring of the image is removed. 
     The first-point detecting means can include first extracting means for extracting a plurality of pixels around the pixel to be subjected to the inner product computation in a predetermined first area from the input image, second extracting means for extracting a plurality of pixels in each of a plurality of second areas contiguous to the first area in a plurality of vertical and horizontal directions, block difference computing means for computing a plurality of block differences by computing the sum of absolute differences between the values of the pixels extracted by the first extracting means and the values of the corresponding pixels extracted by the second extracting means, and difference determining means for determining whether the block difference is greater than a predetermined threshold value. 
     The parameter can be a parameter of the Gaussian function in a model expression representing a relationship between a pixel of a blurred image and a pixel of an unblurred image. 
     The coefficient stored by the storage means can be a coefficient obtained by computing the inverse matrix of the model expression. 
     The selectively-outputting means can include first extracting means for extracting a plurality of pixels subjected to the inner product computation by the inner-product computing means, dispersion computing means for computing the degree of dispersion representing the level of dispersion of the plurality of pixels extracted by the first extracting means, and dispersion determining means for determining whether the degree of dispersion computed by the dispersion computing means is greater than a predetermined threshold value. 
     The selectively-outputting means can further include pixel selecting means for selecting one of the computation result of the inner-product computing means and the value of the pixel of the input image as an output value of the pixel on the basis of the determination result of the dispersion determining means. 
     According to the present invention, an image processing method includes an estimating step for estimating the position of a second point representing a tracking point in an image of a temporally next unit of processing, the second point corresponding to a first point representing the tracking point in an image of a temporally previous unit of processing, a generating step for generating estimated points serving as candidates of the first point when the position of the second point is inestimable, a determining step for determining the second point in the next unit of processing on the basis of the estimation result of the position estimating step when the position of the second point in the next unit of processing is estimable, and a selecting step for selecting the first point from among the estimated points when the position of the second point is inestimable. 
     The determining step can include an evaluation value computing step for computing an evaluation value representing a correlation between pixels of interest representing at least one pixel including the first point in the temporally previous unit of processing and the corresponding pixels representing at least one pixel in the temporally next unit of processing and defined on the basis of a motion vector of the pixel of interest, a variable value computing step for computing a variable value representing the variation of a pixel value with respect to the pixel of interest, and an accuracy computing step for computing the accuracy of the motion vector. 
     The image processing method can further include a first-point detecting step for detecting the first point of a moving object in an image, a correction area setting step for setting a correction area having a predetermined size around the object in the image on the basis of the estimation result, a correcting step for correcting the image in the correction area in the image, and a display control step for controlling the display of the image including the image in the correction area corrected by the correcting step. 
     According to the present invention, a recording medium stores a computer-readable program including an estimating step for estimating the position of a second point representing a tracking point in an image of a temporally next unit of processing, the second point corresponding to a first point representing the tracking point in an image of a temporally previous unit of processing, a generating step for generating estimated points serving as candidates of the first point when the position of the second point is inestimable, a determining step for determining the second point in the next unit of processing on the basis of the estimation result of the position estimating step when the position of the second point in the next unit of processing is estimable, and a selecting step for selecting the first point from among the estimated points when the position of the second point is inestimable. 
     According to the present invention, a program includes program code causing a computer to execute an estimating step for estimating the position of a second point representing a tracking point in an image of a temporally next unit of processing, the second point corresponding to a first point representing the tracking point in an image of a temporally previous unit of processing, a generating step for generating estimated points serving as candidates of the first point when the position of the second point is inestimable, a determining step for determining the second point in the next unit of processing on the basis of the estimation result of the position estimating step when the position of the second point in the next unit of processing is estimable, and a selecting step for selecting the first point from among the estimated points when the position of the second point is inestimable. 
     According to the present invention, if the position of the second point in the subsequent unit of processing is estimable, the second point in the subsequent unit of processing is determined on the basis of the estimation result of the position. If the position of the second point in the subsequent unit of processing is inestimable, the first point is selected from among the estimated points generated. 
     Advantages 
     According to the present invention, tracking of a tracking point in an image can be provided. In particular, the robust performance of tracking can be improved. As a result, the tracking point can be reliably tracked even when the tracking point temporarily disappears due to the rotation of an object to be tracked or even when occlusion or a scene change occurs. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an exemplary configuration of an object tracking apparatus according to the present invention; 
         FIG. 2  is a flow chart illustrating a tracking process performed by the object tracking apparatus shown in  FIG. 1 ; 
         FIG. 3  is a diagram illustrating a tracking process when an object to be tracked rotates; 
         FIG. 4  is a diagram illustrating a tracking process when occlusion occurs; 
         FIG. 5  is a diagram illustrating a tracking process when a scene change occurs; 
         FIG. 6  is a flow chart illustrating normal processing at step S 1  shown in  FIG. 2 ; 
         FIG. 7  is a flow chart illustrating an initialization process of the normal processing at step S 21  shown in  FIG. 6 ; 
         FIG. 8  is a diagram illustrating a transfer candidate extracting process; 
         FIG. 9  is a block diagram of an exemplary configuration of a region-estimation related processing unit; 
         FIG. 10  is a flow chart illustrating a region-estimation related process at step S 26  shown in  FIG. 6 ; 
         FIG. 11  is a flow chart illustrating a region estimation process at step S 61  shown in  FIG. 10 ; 
         FIG. 12A  is a diagram illustrating a process to determine sample points at step S 81  shown in  FIG. 11 ; 
         FIG. 12B  is a diagram illustrating the process to determine sample points at step S 81  shown in  FIG. 11 ; 
         FIG. 13A  is a diagram illustrating the process to determine sample points at step S 81  shown in  FIG. 11 ; 
         FIG. 13B  is a diagram illustrating the process to determine sample points at step S 81  shown in  FIG. 11 ; 
         FIG. 14A  is a diagram illustrating the process to determine sample points at step S 81  shown in  FIG. 11 ; 
         FIG. 14B  is a diagram illustrating the process to determine sample points at step S 81  shown in  FIG. 11 ; 
         FIG. 15  is a diagram illustrating the process to determine sample points at step S 81  shown in  FIG. 11 ; 
         FIG. 16  is a flow chart illustrating a process to update a region estimation range at step S 86  shown in  FIG. 11 ; 
         FIG. 17A  is a diagram illustrating the process to update a region estimation range; 
         FIG. 17B  is a diagram illustrating the process to update a region estimation range; 
         FIG. 17C  is a diagram illustrating the process to update a region estimation range; 
         FIG. 18A  is a diagram illustrating the process to update a region estimation range; 
         FIG. 18B  is a diagram illustrating the process to update a region estimation range; 
         FIG. 18C  is a diagram illustrating the process to update a region estimation range; 
         FIG. 19A  is a diagram illustrating the process to update a region estimation range; 
         FIG. 19B  is a diagram illustrating the process to update a region estimation range; 
         FIG. 19C  is a diagram illustrating the process to update a region estimation range; 
         FIG. 20A  is a diagram illustrating the process to update a region estimation range; 
         FIG. 20B  is a diagram illustrating the process to update a region estimation range; 
         FIG. 20C  is a diagram illustrating the process to update a region estimation range; 
         FIG. 21  is a flow chart illustrating another example of the process to update a region estimation range at step S 86  shown in  FIG. 11 ; 
         FIG. 22A  is a diagram illustrating the process to update a region estimation range; 
         FIG. 22B  is a diagram illustrating the process to update a region estimation range; 
         FIG. 22C  is a diagram illustrating the process to update a region estimation range; 
         FIG. 22D  is a diagram illustrating the process to update a region estimation range; 
         FIG. 23  is a flow chart illustrating the transfer candidate extracting process at step S 62  shown in  FIG. 10 ; 
         FIG. 24  is a flow chart illustrating a template generating process at step S 63  shown in  FIG. 10 ; 
         FIG. 25  is a diagram illustrating the template generation; 
         FIG. 26  is a diagram illustrating the template generation; 
         FIG. 27  is a diagram illustrating a positional relationship between a template and a tracking point; 
         FIG. 28  is a block diagram of another example of the configuration of a region-estimation related processing unit shown in  FIG. 1 ; 
         FIG. 29  is a flow chart illustrating another example of the region estimation process at step S 61  shown in  FIG. 10 ; 
         FIG. 30A  is a diagram illustrating the growth of the same color region; 
         FIG. 30B  is a diagram illustrating the growth of the same color region; 
         FIG. 30C  is a diagram illustrating the growth of the same color region; 
         FIG. 31  is a diagram illustrating the same color region of the tracking point and a region estimation result; 
         FIG. 32  is a flow chart illustrating another example of the transfer candidate extracting process at step S 62  shown in  FIG. 10 ; 
         FIG. 33  is a flow chart illustrating exception processing at step S 2  shown in  FIG. 2 ; 
         FIG. 34  is a flow chart illustrating an initialization process of the exception processing at step S 301  shown in  FIG. 33 ; 
         FIG. 35  is a diagram illustrating template selection; 
         FIG. 36  is a diagram illustrating a search area; 
         FIG. 37  is a flow chart illustrating a continuation determination process at step S 305  shown in  FIG. 33 ; 
         FIG. 38  is a flow chart illustrating another example of the normal processing at step S 1  shown in  FIG. 2 ; 
         FIG. 39  is a flow chart illustrating another example of the region estimation process at step S 61  shown in  FIG. 10 ; 
         FIG. 40  is a flow chart illustrating another example of the transfer candidate extracting process at step S 62  shown in  FIG. 10 ; 
         FIG. 41  is a diagram illustrating a transfer candidate when the normal processing shown in  FIG. 6  is executed; 
         FIG. 42  is a diagram illustrating a transfer candidate when the normal processing shown in  FIG. 38  is executed; 
         FIG. 43  is a block diagram of an exemplary configuration of a motion estimation unit shown in  FIG. 1 ; 
         FIG. 44  is a flow chart illustrating a motion computing process; 
         FIG. 45  is a diagram illustrating a temporal flow of a frame; 
         FIG. 46  is a diagram illustrating a block of the frame; 
         FIG. 47  is a diagram illustrating a block matching method; 
         FIG. 48  is a diagram illustrating a motion vector; 
         FIG. 49  is a flow chart illustrating a motion-vector accuracy computing process; 
         FIG. 50  is a diagram illustrating a method for computing an evaluation value; 
         FIG. 51  is a diagram illustrating an activity computing process; 
         FIG. 52  is a diagram illustrating a method for computing the activity; 
         FIG. 53A  is a diagram illustrating a method for computing the block activity; 
         FIG. 53B  is a diagram illustrating a method for computing the block activity; 
         FIG. 53C  is a diagram illustrating a method for computing the block activity; 
         FIG. 53D  is a diagram illustrating a method for computing the block activity; 
         FIG. 53E  is a diagram illustrating a method for computing the block activity; 
         FIG. 53F  is a diagram illustrating a method for computing the block activity; 
         FIG. 54  is a flow chart illustrating a threshold process; 
         FIG. 55  is a diagram illustrating a relationship between an evaluation value and the activity. 
         FIG. 56  is a flow chart illustrating a normalization process; 
         FIG. 57  is a flow chart illustrating an integrating process; 
         FIG. 58  is a block diagram of an exemplary configuration of a background motion estimation unit; 
         FIG. 59  is a flow chart illustrating a background motion estimation process; 
         FIG. 60  is a block diagram of an exemplary configuration of a scene change detection unit; 
         FIG. 61  is a flow chart illustrating a scene change detection process; 
         FIG. 62  is a block diagram of an exemplary configuration of a television receiver; 
         FIG. 63  is a flow chart illustrating the image display process of the television receiver; 
         FIG. 64  is a block diagram of an exemplary configuration of an image processing apparatus according to the present invention; 
         FIG. 65  is a block diagram of an exemplary configuration of a motion vector accuracy computing unit; 
         FIG. 66  is a block diagram of an exemplary configuration of the image processing apparatus; 
         FIG. 67  is a block diagram of an exemplary configuration of an encoding unit; 
         FIG. 68  is a flow chart illustrating the encoding process of the encoding unit; 
         FIG. 69  is a block diagram of an exemplary configuration of a camera-shake blur correction apparatus; 
         FIG. 70  is a block diagram of an exemplary configuration of a background motion detection unit; 
         FIG. 71  is a flow chart illustrating the camera-shake blur correction process of the camera-shake blur correction apparatus; 
         FIG. 72  is a block diagram of an exemplary configuration of an accumulating apparatus; 
         FIG. 73  is a block diagram of an exemplary configuration of a scene change detection unit; 
         FIG. 74  is a flow chart illustrating the index image generation process of the accumulating apparatus; 
         FIG. 75  is a flow chart illustrating the image output process of the accumulating apparatus; 
         FIG. 76  is a block diagram of an exemplary configuration of a security camera system; 
         FIG. 77  is a flow chart illustrating the monitoring process of the security camera system; 
         FIG. 78  is a block diagram of another configuration of the security camera system; 
         FIG. 79  is a flow chart illustrating the monitoring process of the security camera system; 
         FIG. 80  is a block diagram of an exemplary configuration of a security camera system according to the present invention; 
         FIG. 81  is a flow chart illustrating a monitoring process; 
         FIG. 82A  is a diagram illustrating an example of an image displayed by the security camera system; 
         FIG. 82B  is a diagram illustrating an example of an image displayed by the security camera system; 
         FIG. 82C  is a diagram illustrating an example of an image displayed by the security camera system; 
         FIG. 83  is a diagram illustrating an example of the movement of a correction area; 
         FIG. 84  is a block diagram of an exemplary configuration of an image correction unit; 
         FIG. 85  is a block diagram of an example of a control signal of the image correction unit; 
         FIG. 86A  is a diagram illustrating the principle of image blurring; 
         FIG. 86B  is a diagram illustrating the principle of image blurring; 
         FIG. 86C  is a diagram illustrating the principle of image blurring; 
         FIG. 87  is a diagram illustrating the principle of image blurring; 
         FIG. 88  is a diagram illustrating the principle of image blurring; 
         FIG. 89  is a diagram illustrating the principle of image blurring; 
         FIG. 90  is a diagram illustrating an example of the combination of parameter codes; 
         FIG. 91  is a diagram illustrating an edge portion of an image; 
         FIG. 92  is a flow chart illustrating a blur correction process; 
         FIG. 93  is a flow chart illustrating an image correction process; 
         FIG. 94  is a flow chart illustrating an image feature detection process; 
         FIG. 95  is a diagram illustrating an exemplary configuration of an image feature detection unit; 
         FIG. 96A  is a diagram illustrating a block of an image extracted by a block cutout unit; 
         FIG. 96B  is a diagram illustrating a block of an image extracted by the block cutout unit; 
         FIG. 96C  is a diagram illustrating a block of an image extracted by the block cutout unit; 
         FIG. 96D  is a diagram illustrating a block of an image extracted by the block cutout unit; 
         FIG. 96E  is a diagram illustrating a block of an image extracted by the block cutout unit; 
         FIG. 97  is a flow chart illustrating an image combining process; 
         FIG. 98  is a block diagram illustrating an exemplary configuration of an image combining unit; and 
         FIG. 99  is a diagram illustrating the dispersion computation. 
     
    
    
     BEST MODE FOR CARRYING OUT THE INVENTION 
     Exemplary Embodiments of the present invention are now herein described with reference to the accompanying drawings. 
       FIG. 1  is a functional block diagram of an object tracking apparatus including an image processing apparatus according to the present invention. An object tracking apparatus  1  includes a template matching unit  11 , a motion estimation unit  12 , a scene change detection unit  13 , a background motion estimation unit  14 , a region-estimation related processing unit  15 , a transfer candidate storage unit  16 , a tracking point determination unit  17 , a template storage unit  18 , and a control unit  19 . 
     The template matching unit  11  performs a matching process between an input image and a template image stored in the template storage unit  18 . The motion estimation unit estimates the motion of the input image and outputs a motion vector obtained from the estimation and the accuracy of the motion vector to the scene change detection unit  13 , the background motion estimation unit  14 , the region-estimation related processing unit  15 , and the tracking point determination unit  17 . The configuration of the motion estimation unit  12  is described in detail below with reference to  FIG. 43 . 
     The scene change detection unit  13  detects a scene change on the basis of the accuracy received from the motion estimation unit  12 . The configuration of the scene change detection unit  13  is described in detail below with reference to  FIG. 50 . 
     The background motion estimation unit  14  estimates the motion of a background on the basis of the motion vector and the accuracy received from the motion estimation unit  12  and delivers the estimation result to the region-estimation related processing unit  15 . The configuration of the background motion estimation unit  14  is described in detail below with reference to  FIG. 48 . 
     The region-estimation related processing unit  15  performs a region estimation process on the basis of the motion vector and the accuracy delivered from the motion estimation unit  12 , the motion of the background delivered from the background motion estimation unit  14 , and the tracking point information delivered from the tracking point determination unit  17 . The region-estimation related processing unit  15  also generates a transfer candidate on the basis of the input information and delivers the transfer candidate to the transfer candidate storage unit  16 , which stores the transfer candidate. Furthermore, the region-estimation related processing unit  15  generates a template on the basis of the input image and delivers the template to the template storage unit  18 , which stores the template. The configuration of the region-estimation related processing unit  15  is described in detail below with reference to  FIG. 9 . 
     The tracking point determination unit  17  determines a tracking point on the basis of the motion vector and the accuracy delivered from the motion estimation unit  12  and the transfer candidate delivered from the transfer candidate storage unit  16  and outputs information about the determined tracking point to the region-estimation related processing unit  15 . 
     The control unit  19  is connected to each of the units from the template matching unit  11  through the template storage unit  18 . The control unit  19  controls each unit on the basis of a tracking point instruction input by a user so as to output the tracking result to a device (not shown). 
     The operation of the object tracking apparatus  1  is described next. 
     As shown in  FIG. 2 , the object tracking apparatus  1  basically performs normal processing and exception processing. That is, the object tracking apparatus  1  performs the normal processing at step S 1 . The normal processing is described below with reference to  FIG. 6 . In this processing, a process for tracking a tracking point specified by the user is performed. If the object tracking apparatus  1  cannot transfer the tracking point to a new tracking point in this normal processing at step S 1 , the exception processing is performed at step S 2 . The exception processing is described in detail below with reference to  FIG. 33 . When the tracking point disappears from the image, the exception processing performs an operation to return to the normal processing by using a template matching operation. In the exception processing, if it is determined that the tracking operation cannot continue (i.e., the processing cannot return to the normal processing), the processing is completed. However, if it is determined that the processing can return to the normal processing as a result of the returning process using the template, the processing returns to step S 1  again. Thus, the normal processing at step S 1  and the exception processing at step S 2  are alternately repeated for each frame. 
     According to the present invention, as shown in  FIGS. 3 to 5 , by performing the normal processing and the exception processing, the object tracking apparatus  1  can track the tracking point even when the tracking point temporarily disappears due to the rotation of the object to be tracked, the occurrence of occlusion, and the occurrence of a scene change. 
     That is, for example, as shown in  FIG. 3 , a human face  504 , which is an object to be tracked, is displayed in a frame n−1. The human face  504  includes a right eye  502  and a left eye  503 . The user specifies, for example, the right eye  502  (precisely speaking, one pixel in the right eye  502 ) as a tracking point  501 . In an example shown in  FIG. 3 , the person moves to the left in the drawing in the next frame n. Furthermore, the human face  504  rotates clockwise in the next frame n+1. As a result, the right eye  502  that has been visible disappears. Thus, in a known method, the tracking cannot be performed. Therefore, in the normal processing at step S 1 , the left eye  503  of the human face  504  is considered to be an object similar to the right eye and is selected so that the tracking point is transferred (set) to the left eye  503 . Thus, the tracking can be continued. 
     In an example shown in  FIG. 4 , in a frame n−1, a ball moves from the left of the human face  504 . In the next frame n, the ball  521  exactly covers the human face  504 . In this state, the human face  504  including the right eye  502 , which is specified as the tracking point  501 , is not displayed. If such occlusion occurs and the human face  504 , which is the object to be tracked, is not displayed, the transfer point in place of the tracking point  501  disappears. Accordingly, it is difficult to maintain tracking of the tracking point. However, according to the present invention, the image of the right eye  502  serving as the tracking point in the frame n−1 (in practice, temporally more previous frame) is stored as a template in advance. When the ball further moves to the right and the right eye  502  serving as the tracking point  501  appears again in the frame n+1, the object tracking apparatus  1  detects that the right eye serving as the tracking point  501  is displayed again through the exception processing at step S 2 . Thus, the right eye  502  is tracked as the tracking point  501  again. 
     In an example shown in  FIG. 5 , the human face  504  is displayed in the frame n−1. However, in the next frame n, a motor vehicle  511  covers the whole body including the human face. That is, in this case, a scene change occurs. According to the present invention, even when such a scene change occurs and the tracking point  501  disappears from the image, the object tracking apparatus  1  can detect that the right eye  502  serving as the tracking point  501  is displayed again in the exception processing at step S 2  using the template when the motor vehicle  511  moves and the right eye is displayed again in a frame n+1. Thus, the right eye  502  can be tracked as the tracking point  501  again. 
     The normal processing at step S 1  shown in  FIG. 2  is described in detail next with reference to a flow chart shown in  FIG. 6 . At step S 21 , the tracking point determination unit  17  executes the initialization process of the normal processing. The initialization process is described below with reference to a flow chart shown in  FIG. 7 . In this initialization process, a region estimation range with respect to a tracking point specified by the user is selected. This region estimation range is used to estimate the range of points belonging to an object that is the same as the user-specified tracking point (e.g., a human face or body serving as a rigid-body that moves along with an eye when the tracking point is the eye). The transfer point is selected from among the points in the region estimation range. 
     At step S 22 , the control unit  19  controls each unit to wait for the input of an image of the next frame. At step S 23 , the motion estimation unit  12  estimates the motion of the tracking point. That is, by receiving a frame (next frame) temporally next to a frame (previous frame) that includes a user-specified tracking point at step S 22 , the control unit  19  can acquire the images in two consecutive frames. Accordingly, at step S 23 , by estimating the position of the tracking point in the next frame corresponding to the tracking point in the previous frame, the motion of the tracking point can be estimated. 
     As used herein, the term “temporally previous” refers to the order of processing (the order of input). In general, images of frames are input in the order of capturing the images. In this case, the frame captured earlier is defined as a previous frame. However, when the frame captured later is processed (input) first, the frame captured later is defined as a previous frame. 
     At step S 24 , the motion estimation unit  12  (an integration processing unit  605  shown in  FIG. 43 , which is described below) determines whether the tracking point can be estimated on the basis of the processing result at step S 23 . It can be determined whether the tracking point can be estimated or not by, for example, comparing the accuracy of a motion vector generated and output from the motion estimation unit  12  (which is described below with reference to  FIG. 43 ) with a predetermined threshold value. More specifically, if the accuracy of the motion vector is greater than or equal to the predetermined threshold value, the tracking point can be estimated. However, if the accuracy of the motion vector is less than the predetermined threshold value, it is determined that the tracking point cannot be estimated. That is, the possibility of the estimation here is relatively strictly determined. Even when the estimation is possible in practice, the estimation is determined to be impossible if the accuracy is low. Thus, a more reliable tracking process can be provided. 
     It can be determined at step S 24  that the estimation is possible if the estimation result of the motion of the tracking point and the estimation results of the motion of the points in the vicinity of the tracking point coincide with the numerically predominant motions; if otherwise, the estimation is not possible. 
     If it is determined that the motion of the tracking point can be estimated, that is, if it is determined that the probability that the tracking point is correctly set on the same object (the probability of correctly tracking the right eye  502  when the right eye  502  is specified as the tracking point  501 ) is relatively high, the process proceeds to step S 25 . At step S 25 , the tracking point determination unit  17  shifts the tracking point by the estimated motion (motion vector) obtained at step S 23 . That is, after this operation is executed, the tracking point in the next frame that is the tracking point corresponding to the tracking point in the previous frame can be determined. 
     After the process at step S 25  is executed, a region estimation related process is carried out at step S 26 . This region estimation related process is described in detail below with reference to  FIG. 10 . By carrying out this process, the region estimation range determined by the initialization process of the normal processing at step S 21  is updated. Furthermore, when the tracking point is not displayed due to, for example, the rotation of the target object, candidates of a transfer point to which the tracking point is to be transferred (transfer candidates) are extracted (generated) in advance in this state (i.e., in the state in which tracking the tracking point is still maintained). When even the transfer to the transfer candidate is not possible, the tracking is temporarily stopped. However, a template is created in advance in order to confirm that the tracking is possible again (i.e., the tracking point appears again). 
     After the region estimation related process at step S 26  is completed, the processing returns to step S 22  and the processes subsequent to step S 22  are repeated. 
     That is, as long as the motion of the user-specified tracking point can be estimated, the processes from step S 22  through S 26  are repeated for each frame so that the tracking is carried out. 
     However, if, at step S 24 , it is determined that the motion of the tracking point cannot be estimated (the estimation is impossible), that is, if it is determined that, for example, the accuracy of the motion vector is less than or equal to the threshold value, the process proceeds to step S 27 . At step S 27 , since the transfer candidates generated by the region estimation related process at step S 26  are stored in the transfer candidate storage unit  16 , the tracking point determination unit  17  selects one candidate that is the closest to the original tracking point from among the candidates stored in the transfer candidate storage unit  16 . At step S 28 , the tracking point determination unit  17  determines whether a transfer candidate can be selected. If a transfer candidate can be selected, the process proceeds to step S 29 , where the tracking point is transferred (changed) to the transfer candidate selected at step S 27 . That is, the point indicated by the transfer candidate is set as a new tracking point. Thereafter, the processing returns to step S 23 , where the motion of the tracking point selected from the transfer candidates is estimated. 
     At step S 24 , it is determined whether the motion of the newly set tracking point can be estimated. If the estimation is possible, the tracking point is sifted by the amount of the estimated motion at step S 25 . At step S 26 , the region estimation related process is carried out. Thereafter, the processing returns to step S 22  again and the processes subsequent to step S 22  are repeated. 
     If, at step S 24 , it is determined that the motion of the newly set tracking point cannot be estimated, the processing returns to step S 27  again. At step S 27 , a transfer candidate that is the next closest to the original tracking point is selected. At step S 29 , the selected transfer candidate is set to a new tracking point. The processes subsequent to step S 23  are repeated again for the newly set tracking point. 
     If the motion of the tracking point cannot be estimated after every prepared transfer candidate is set to a new tracking point, it is determined at step S 28  that the transfer candidate cannot be selected. Thus, the normal processing is completed. Thereafter, the process proceeds to the exception processing at step S 2  shown in  FIG. 2 . 
     The initialization operation of the normal processing at step S 21  shown in  FIG. 6  is described in detail with reference to a flow chart shown in  FIG. 7 . 
     At step S 41 , the control unit  19  determines whether the current processing is a return processing from the exception processing. That is, the control unit  19  determines whether the processing has returned to the normal processing again after the exception processing at step S 2  was completed. Since the exception processing at step S 2  has not been executed for the first frame, it is determined that the processing is not a return processing from the exception processing. Thus, the process proceeds to step S 42 . At step S 42 , the tracking point determination unit  17  sets the tracking point to the point specified as a tracking point. That is, the user specifies a predetermined point in the input image as the tracking point for the control unit  19  by operating a mouse or another input unit (not shown). On the basis of this instruction, the control unit  19  controls the tracking point determination unit  17  to determine the point specified by the user to be the tracking point. Alternatively, the tracking point may be determined by using another method. For example, the point having the highest brightness may be determined to be the tracking point. The tracking point determination unit  17  delivers information about the determined tracking point to the region-estimation related processing unit  15 . 
     At step S 43 , the region-estimation related processing unit  15  determines the region estimation range on the basis of the position of the tracking point determined at step S 42 . The region estimation range is a range that is referenced when points on the solid body including the tracking point are estimated. The region estimation range is determined in advance so that the solid body including the tracking point dominantly occupies the region estimation range. More specifically, the region estimation range is determined so that the position and the size follow the solid body including the tracking point, and therefore, the portion in the region estimation range that exhibits the numerically predominant movements can be estimated to belong to the solid body including the tracking point. At step S 43 , for example, a predetermined constant area at the center of which is the tracking point is determined to be the region estimation range as an initial value. 
     Subsequently, the process proceeds to step S 22  shown in  FIG. 3 . 
     In contrast, if, at step S 41 , it is determined that the current processing is a return processing from the exception processing at step S 2 , the process proceeds to step S 44 . At step S 44 , the tracking point determination unit  17  determines the tracking point and the region estimation range on the basis of the position that matches the template in a process at step S 303  shown in  FIG. 33 , which is described below. For example, a point in the current frame that matches the tracking point in the template is determined to be the tracking point. Also, the predetermined constant area around that point is determined to be the region estimation range. Thereafter, the process proceeds to step S 22  shown in  FIG. 3 . 
     The above-described processing is described next with reference to  FIG. 8 . That is, at step S 42  shown in  FIG. 7 , as shown in  FIG. 8 , if the right eye  502  in a frame n−1, for example, is specified as the tracking point  501 , a predetermined area including the tracking point  501  is specified as a region estimation range  533  at step S 43 . At step S 24 , it is determined whether a sample point within the region estimation range  533  can be estimated in the next frame. In the example shown in  FIG. 8 , in the frame n+1 subsequent to the frame n, since the left half area  534  including the right eye  502  is covered by the ball  521 , the motion of the tracking point  501  in the frame n cannot be estimated in the next frame n+1. Therefore, in such a case, one point is selected from among points in the region estimation range  533  (the face  504  as a solid body including the right eye  502 ) prepared as the transfer candidates in advance in the temporary previous frame n−1. For example, the left eye  503  contained in the human face  504  and, more precisely, one pixel in the left eye  503  is selected here. The selected point is determined to be the tracking point in the frame n+1. 
     The region-estimation related processing unit  15  has a configuration shown in  FIG. 9  in order to carry out the region-estimation related processing at step S 26  shown in  FIG. 6 . That is, a region estimation unit  41  of the region-estimation related processing unit  15  receives a motion vector and the accuracy from the motion estimation unit  12 , receives the background motion from the background motion estimation unit  14 , and receives the positional information about the tracking point from the tracking point determination unit  17 . A transfer candidate extraction unit  42  receives the motion vector and the accuracy from the motion estimation unit  12 . The transfer candidate extraction unit  42  also receives the output from the region estimation unit  41 . A template generation unit  43  receives the input image and the output from the region estimation unit  41 . 
     The region estimation unit  41  estimates the region of the solid body including the tracking point on the basis of the inputs and, subsequently, outputs the estimation result to the transfer candidate extraction unit  42  and the template generation unit  43 . The transfer candidate extraction unit  42  extracts the transfer candidates on the basis of the inputs and, subsequently, delivers the extracted transfer candidates to the transfer candidate storage unit  16 . The template generation unit  43  generates a template on the basis of the inputs and, subsequently, delivers the generated template to the template storage unit  18 . 
       FIG. 10  illustrates the region-estimation related process performed by the region-estimation related processing unit  15  (the process at step S 26  shown in  FIG. 6 ) in detail. At step S 61 , the region estimation process is executed by the region estimation unit  41 . The detailed operation is described below with reference to a flow chart shown in  FIG. 11 . In this process, points in a region of an image estimated to belong to an object that is the same as the object to which the tracking point belongs (a solid body moving in synchronization with the tracking point) are extracted as a region estimation range (a region estimation range  81  in  FIG. 17  described below). 
     At step S 62 , a transfer candidate extraction process is executed by the transfer candidate extraction unit  42 . This process is described in detail below with reference to a flow chart shown in  FIG. 23 . The points of the transfer candidate are extracted from the points in the range estimated to be the region estimation range by the region estimation unit  41 . The extracted points are stored in the transfer candidate storage unit  16 . 
     At step S 63 , a template generation process is executed by the template generation unit  43 . This process is described in detail below with reference to a flow chart shown in  FIG. 24 . A template is generated by this process. 
     The region estimation process at step S 61  shown in  FIG. 10  is described next with reference to a flow chart shown in  FIG. 11 . 
     At step S 81 , the region estimation unit  41  determines sample points serving as candidate points estimated to be the points belonging to the object including the tracking point. 
     For example, as shown in  FIG. 12 , the sample points (indicated by black squares) can be the pixels at positions spaced from each other by predetermined pixels in the horizontal direction and the vertical direction starting from a fixed reference point  541 . In the example shown in  FIG. 12 , the pixel at the upper left corner of each frame is defined as the reference point  541  (indicated by the symbol “x” in the drawing). The sample points are pixels at positions spaced from each other by 5 pixels in the horizontal direction and by 5 pixels in the vertical direction starting from the reference point  541 . That is, in this example, pixels dispersed in the entire screen are defined as the sample points. Also, in this example, the reference points in the frames n and n+1 are the same at a fixed position. 
     For example, as shown in  FIG. 13 , the reference point  541  may be dynamically changed so that the reference point in the frame n and the reference point in the frame n+1 are located at different positions. 
     In the examples shown in  FIGS. 12 and 13 , the distance between the sample points is constant for each frame. However, as shown in  FIG. 14 , the distance between the sample points may be changed for each frame. In the example shown in  FIG. 14 , the distance between the sample points is 5 pixels in the frame n, whereas the distance between the sample points is 8 pixels in the frame n+1. At that time, the dimensions of the region estimated to belong to the object including the tracking point can be used as a reference distance. More specifically, as the dimensions of the region estimation range decrease, the distance decreases. 
     Alternatively, as shown in  FIG. 15 , the distances between the sample points may be changed from each other in one frame. At that time, the distance between the sample point and the tracking point may be used as a reference distance. That is, as the sample points are closer to the tracking point, the distance between the sample points decreases. In contrast, as the sample points are more distant from the tracking point, the distance between the sample points increases. 
     Thus, the sample points are determined. Subsequently, at step S 82 , the region estimation unit  41  executes a process for estimating the motions of the sample points in the region estimation range (determined at steps S 43  and S 44  in  FIG. 7  or at steps S 106  and S 108  in  FIG. 16 , which is described below). That is, the region estimation unit  41  extracts points in the next frame corresponding to the sample points in the region estimation range on the basis of the motion vector delivered from the motion estimation unit  12 . 
     At step S 83 , the region estimation unit  41  executes a process for removing points based on the motion vectors having the accuracy lower than a predetermined threshold value from the sample points estimated at step S 82 . The accuracy of motion vectors required for executing this process is provided by the motion estimation unit  12 . Thus, from among the sample points in the region estimation range, only the points estimated on the basis of the motion vectors having high accuracy are extracted. 
     At step S 84 , the region estimation unit  41  extracts the full-screen motion on the basis of the estimation result of the motions in the region estimation range. As used herein, the term “full-screen motion” refers to a motion of a region having the largest size among regions having the same motion. More specifically, to the motion of each sample point, a weight that is proportional to the intersample distance of the sample point is assigned so that the histogram of the motion is created. The motion (one motion vector) that maximizes the frequency of weighting is extracted as the full-screen motion. When creating the histogram, for example, the representative value of the motion may be prepared in consideration of the pixel resolution. The motion having a difference by one pixel resolution may be added to the histogram. 
     At step S 85 , the region estimation unit  41  extracts sample points in the region estimation range having the full-screen motion as a result of the region estimation. Here, as the sample points having a full-screen motion, not only the sample point having the same motion as the full-screen motion is extracted, but also a sample point having a motion different from the full-screen motion by less than or equal to a predetermined threshold value can be extracted. 
     Thus, of the sample points in the region estimation range determined at step S 43 , S 44 , S 44 , S 106 , or S 108 , sample points having the full-screen motion is finally extracted (generated) as the points estimated to belong to the object including the tracking point. 
     Thereafter, at step S 86 , the region estimation unit  41  executes a process for updating the region estimation range. The processing then proceeds to step S 22  shown in  FIG. 6 . 
       FIG. 16  illustrates the process to update the region estimation range at step S 86  shown in  FIG. 11  in detail. At step S 101 , the region estimation unit  41  computes the center of gravity of a region. This region refers to the region defined by the sample points extracted at step S 85  shown in  FIG. 11  (i.e., the region defined by the points estimated to belong to the object including the tracking point). That is, there is a one-to-one correspondence between a motion vector (full-screen motion) and this region. For example, as shown in  FIG. 17A , from among sample points indicated by white squares within a region estimation range  81 , sample points indicated by black squares are extracted as sample points having the full-screen motion at step S 85  shown in  FIG. 11 . The region defined by these sample points is extracted (estimated) as a region  82 . Thereafter, the center of gravity  84  of the region  82  is computed. More specifically, a weight according to the intersample distance is assigned to each sample point, and a sample point gravity is computed as the center of gravity of the region. This process is executed to find the position of the region in the current frame. 
     At step S 102 , the region estimation unit  41  shifts the center of gravity of the region in accordance with the full-screen motion. This process is executed so that the region estimation range  81  follows the motion of the position of the region and moves the region to the estimated position in the next frame. As shown in  FIG. 17B , when the tracking point  83  in the current frame appears as a tracking point  93  in the next frame in accordance with a motion vector  88  of the tracking point  83 , a motion vector  90  of the full-screen motion substantially corresponds to the motion vector  88 . Accordingly, by shifting the center of gravity  84  in the current frame on the basis of the motion vector (full-screen motion)  90 , a point  94  in the frame same as that of the tracking point  93  (the next frame) can be obtained. By setting a region estimation range  91  at the center of which is the point  94 , the region estimation range  81  can follow the motion of the position of the region  82  so as to move to the estimated position in the next frame. 
     At step S 103 , the region estimation unit  41  determines the size of the next region estimation range on the basis of the region estimation result. More specifically, square sum of the distances between all the sample points estimated to be the region (the distances between the black squares in the region  82  shown in  FIG. 17 ) is considered to be the dimensions of the region  82 . The size of a region estimation range  91  in the next frame is determined so as to be slightly larger than the dimensions of the region  82 . That is, as the number of sample points in the region  82  increases, the size of the region estimation range  91  increases. In contrast, as the number of sample points in the region  82  decreases, the size of the region estimation range  91  decreases. Thus, the size of the region estimation range  91  can not only follow the enlargement and reduction of the region  82  but also prevent the full screen region in the region estimation range  81  from being the peripheral area of the tracking object. 
     If the full-screen motion extracted at step S 84  shown in  FIG. 11  is equal to the background motion, the tracking object cannot be distinguished from the background by the motion. Therefore, the background motion estimation unit  14  executes a process for estimating a background motion at all times (the details are described below with reference to  FIG. 49 ). At step S 104 , the region estimation unit  41  determines whether the background motion delivered from the background motion estimation unit  14  is equal to the full-screen motion extracted at step S 84  shown in  FIG. 11 . If the full-screen motion is equal to the background motion, the region estimation unit  41 , at step S 105 , limits the size of the next region estimation range so that the size of the current region estimation range is maximized. Consequently, the background is not erroneously identified as the tracking object. Thus, the size of the region estimation range is controlled so as not to be enlarged. 
     If, at step S 104 , it is determined that the full-screen motion is not equal to the background motion, the process at step S 105  is not necessary, and therefore, the process at step S 105  is skipped. 
     At step S 106 , the region estimation unit  41  determines the size of the next region estimation range at the center of which is the center of gravity of the region after the shift. Thus, the region estimation range is determined so that the center of gravity of the region estimation range is equal to the obtained center of gravity of the region after the shift and the size of the region estimation range is proportional to the size of the region. 
     In an example shown in  FIG. 17B , the size of the region estimation range  91  at the center of which is the center of gravity  94  after the shift based on the motion vector (full-screen motion)  90  is determined in accordance with the dimensions of the region  82 . 
     It should be ensured that the region having the full-screen motion inside the region estimation range  91  is a region of the object to be tracked (e.g., the face  504  shown in  FIG. 8 ). Therefore, at step S 107 , the region estimation unit  41  determines whether the tracking point is included in the next region estimation range. If the tracking point is not included in the next region estimation range, the region estimation unit  41 , at step S 108 , executes a process to shift the next region estimation range so that the next region estimation range includes the tracking point. If the tracking point is included in the next region estimation range, the process at step S 108  is not necessary, and therefore, the process at step S 108  is skipped. 
     More specifically, in this case, the next region estimation range may be shifted so that the moving distance is minimal. Alternatively, the next region estimation range may be shifted along a vector from the center of gravity of region estimation range to the tracking point by the minimal distance so that the tracking point is included in the next region estimation range. 
     In order to maintain the robust performance of the tracking, the shift of the region to include the tracking point may be skipped. 
     In the example shown in  FIG. 17C , since the region estimation range  91  does not include the tracking point  93 , the region estimation range  91  is shifted to the position indicated by a region estimation range  101  (the position that includes the tracking point  93  at the upper left corner) 
       FIGS. 17A to 17C  illustrate the examples in which the shifting process at step S 108  is required. In contrast,  FIGS. 18A to 18C  illustrate the examples in which the shifting process at step S 108  is not required (i.e., the examples when it is determined at step S 107  that the tracking point is included in the next region estimation range). 
     As shown in  FIGS. 18A to 18C , when all the sample points in the region estimation range  81  are points of the region, the need for the shifting process at step S 108  shown in  FIG. 16  is eliminated. 
       FIGS. 17A to 17C  and  FIGS. 18A to 18C  illustrate the examples in which the region estimation range is rectangular. However, as shown in  FIGS. 19A to 19C  and  FIGS. 20A to 20C , the region estimation range can be circular.  FIGS. 19A to 19C  correspond to  FIGS. 17A to 17C , respectively, in which the shifting process at step S 108  is required. In contrast,  FIGS. 20A to 20C  correspond to  FIGS. 18A to 18C , respectively, in which the shifting process at step S 108  is not required. 
     Thus, by executing the process for updating the region estimation range shown in  FIG. 16  (at step S 86  shown in FIG.  11 ), the position and the size of the region estimation range for the next frame are determined so that the region estimation range includes the tracking point. 
     In the process for updating the region estimation range shown in  FIG. 16 , the shape of the region estimation range is a fixed rectangle or circle. However, the shape of the region estimation range may be variable. In such an example, a process for updating the region estimation range at step S 86  shown in  FIG. 11  is described next with reference to  FIG. 21 . 
     At step S 131 , the region estimation unit  41  determines whether the full-screen motion extracted at step S 84  shown in  FIG. 11  is equal to the background motion estimated by the background motion estimation unit  14 . If the two are not equal, the process proceeds to step S 133 , where the region estimation unit  41  determines a small region corresponding to every point estimated to belong to the region (the region composed of pixels having a motion equal to the full-screen motion) (i.e., one small region is determined for one point). In the examples shown in  FIGS. 22A and 22B , in a region estimation range  161 , small regions  171  and  172  are determined which correspond to the points in the region indicated by black squares. In the drawing, reference numeral  171  represents an example in which four small regions corresponding to the four points overlap each other. The size of the small region may be determined so as to, for example, be proportional to the distance between the sample points. 
     At step S 134 , the region estimation unit  41  determines the union of the small regions determined at step S 133  to be a temporary region estimation range. In an example shown in  FIG. 22C , a region  182 , which is a union of the regions  171  and  172  is determined to be the temporary region estimation range. If a plurality of noncontiguous regions are created after the union of the small regions is obtained, only the region having the largest dimensions may be determined to be the temporary region estimation range. 
     If, at step S 131 , it is determined that the full-screen motion is equal to the background motion, the region estimation unit  41 , at step S 132 , determines the current region estimation range to be the temporary region estimation range. The reason why the current region estimation range is determined to be the temporary region estimation range is that the current region estimation range is kept unchanged since the background cannot be distinguished from the object to be tracked by the motions when the estimation result of the background motion is equal to the full-screen motion. 
     After the process at step S 134  or S 132  is completed, the region estimation unit  41 , at step  135 , determines the next region estimation range by shifting the temporary region estimation range determined at step S 134  or S 132  using the full-screen motion. In the example shown in  FIG. 22C , a temporary region estimation range  181  is shifted on the basis of a motion vector  183  of the full-screen motion and is determined to be the temporary region estimation range  182 . 
     At step S 136 , the region estimation unit  41  determines whether the tracking point is included in the next region estimation range determined at step S 135 . If the tracking point is not included in the next region estimation range, the process proceeds to step S 137 , where the region estimation unit  41  shifts the next region estimation range so that the next region estimation range includes the tracking point. In the examples shown in  FIGS. 22C and 22D , since the region estimation range  182  does not include a tracking point  184 , the region estimation range  182  is shifted so as to include the tracking point  184  at the upper left corner and is determined to be a region estimation range  191 . 
     If, at step S 136 , it is determined that the tracking point is included in the next region estimation range, the shifting process at step S 137  is not necessary, and therefore, the shifting process at step S 137  is skipped. 
     A process for extracting a transfer candidate at step S 62  shown in  FIG. 10  is described with reference to a flow chart shown in  FIG. 23 . 
     At step S 161 , the transfer candidate extraction unit  42  holds the shifting result of a point shifted by the estimated motion for every point estimated to belong to the region of the full-screen motion as transfer candidates. That is, the points obtained as the region estimation result are not directly used. In order to use these points in the next frame, the process to extract the shifting result on the basis of the motion estimation result thereof is executed. The extracted transfer candidates are then delivered to the transfer candidate storage unit  16  and are stored in the transfer candidate storage unit  16 . 
     This process is described next with reference to  FIG. 8 . That is, in the example shown in  FIG. 8 , the tracking point  501  is present in the frames n−1 and n. However, in the frame n+1, the tracking point  501  is covered by the ball  521  coming from the left in the drawing, and therefore, the tracking point  501  disappears. Accordingly, in the frame n+1, the tracking point is required to be transferred to a different point in the face  504  serving as the object to be tracked (for example, transferred to the left eye  503 , and more precisely, the point that is the closest to the right eye  502 ). Therefore, the transfer candidate is prepared in advance in the previous frame before the transfer is actually required. 
     More specifically, in the example shown in  FIG. 8 , it is predictable that, in most cases, the estimation result of the motion in the region estimation range  533  from the frame n to the frame n+1 is not correctly estimated since the transfer is required in the region estimation range  533 . That is, in the example shown in  FIG. 8 , the transfer occurs since the tracking point and part of the object including the tracking point disappear. Thus, for a portion  534  of the region estimation range  533  in the frame n where the object is hidden in the frame n+1 (the portion indicated by cross-hatching in  FIG. 8 ), the motion is not correctly estimated, and therefore, the accuracy of the motion is estimated to be low or not to be low and the estimation result of the motion is meaningless. 
     In this case, since the motion estimation result that can be used for the region estimation decreases or an incorrect motion estimation result get mixed, the possibility increases that the region estimation is incorrect. Additionally, in general, this possibility in the temporally more previous region estimation from the frame n−1 to frame n is lower than that in the region estimation from the frame n to frame n+1. 
     Accordingly, to reduce the risk of the incorrect estimation and increase performance, it is desirable that the region estimation result is not directly used, but the region estimation result obtained in the frame n−1 (or temporally more previous frame) is used as the transfer candidate of the moving target. 
     However, the region estimation result can be directly used. The processing in such a case is described with reference to  FIG. 38 . 
       FIG. 24  illustrates a detailed process for generating a template at step S 63  shown in  FIG. 10 . At step S 181 , the template generation unit  43  determines a small region for every point estimated to belong to the region (the region of the full-screen motion). In an example shown in  FIG. 25 , a small region  222  is determined for a point  221  of the region. 
     At step S 182 , the template generation unit  43  determines the union of the small regions determined at step S 181  to be a template region. In the example shown in  FIG. 25 , the union of the small regions  222  is determined to be a template region  231 . 
     Subsequently, at step S 183 , the template generation unit  43  generates a template from information about the template region determined at step S 182  and image information and delivers the template to the template storage unit  18 , which stores the template. More specifically, pixel data in the template region  231  is determined to be the template. 
     As shown in  FIG. 26 , a small region  241  corresponding to the point  221  of the region is larger than the small region  222  shown in  FIG. 25 . Consequently, a template region  251 , which is the union of the small regions  241 , is also larger than the template region  231  shown in  FIG. 25 . 
     The size of the small region may be proportional to the distance between the sample points. In this case, the constant of proportion can be determined so that the dimensions are equal to the square of the distance between the sample points. Alternatively, the constant of proportion can be determined so that the dimensions are greater than or less than the square of the distance between the sample points. 
     In addition, in place of the region estimation result, a region having a fixed size and shape at the center of which is the tracking point, for example, may be used as the template region. 
       FIG. 27  illustrates a positional relationship between the template and the region estimation range. A template region  303  includes a tracking point  305 . The upper left corner point of a circumscribed rectangle  301  that is circumscribed about the template region  303  is defined as a template reference point  304 . A vector  306  from the template reference point  304  to the tracking point  305  and a vector  307  from the template reference point  304  to a reference point  308  at the upper left corner of a region estimation range  302  serves as information about the template region  303 . The template is composed of pixels included in the template region  303 . The vectors  306  and  307  are used for the process to return to the normal processing when an image that is the same as the template is detected. 
     In the above-described processes, unlike the transfer candidate, the range and pixels corresponding to the current frame are determined to be the template. However, like the transfer candidate, the moving target points in the next frame may be used as the template. 
     Thus, like the transfer candidate, the template composed of pixel data including the tracking point is generated in advance during the normal processing. 
     The region estimation related process at step S 26  shown in  FIG. 6  can be executed by the region-estimation related processing unit  15  having, for example, the configuration shown in  FIG. 28 . 
     In this case, like the region-estimation related processing unit  15  shown in  FIG. 9 , the region-estimation related processing unit  15  includes the region estimation unit  41 , the transfer candidate extraction unit  42 , and the template generation unit  43 . In this embodiment, information about a tracking point and an input image are input from the tracking point determination unit  17  to the region estimation unit  41 . Only the output of the region estimation unit  41  is input to the transfer candidate extraction unit  42 . The output of the region estimation unit  41  and the input image are input to the template generation unit  43 . 
     In this case, like the process shown in  FIG. 10 , the region estimation process is performed at step S 61 , the transfer candidate extraction process is performed at step S 62 , and the template generation process is performed at step S 63 . Since the template generation process performed at step S 63  is identical to the process shown in  FIG. 24 , only the region estimation process at step S 61  and the transfer candidate extraction process at step S 62  are described next. 
     First, the region estimation process at step S 61  is described in detail with reference to a flow chart shown in  FIG. 29 . At step S 201 , the region estimation unit  41  shown in  FIG. 28  determines a sample point in order to estimate a region in an image that belongs to an object including the tracking point. This process is identical to the process at step S 81  shown in  FIG. 11 . 
     However, the frame to be processed at step S 201  is the frame in which the tracking point has been determined (the frame including the tracking point after tracking is completed). This is different from step S 81  shown in  FIG. 11  in which the frame used for determining sample points is the previous frame. 
     Subsequently, at step S 202 , the region estimation unit  41  executes a process to apply a low-pass filter in the spatial direction to an image of the next frame (the frame in which the sample points are determined at step S 201 ). That is, by applying a low-pass filter, a high-frequency component is removed from the image and the image is smoothed. Thus, a growth process of the same color region at subsequent step S 203  is facilitated. 
     At step S 203 , the region estimation unit  41  executes a process for growing the same color region including the tracking point from the tracking point serving as a starting point under the condition that the difference between pixel values is less than a threshold value THimg and defines sample points included in the same color region as an estimation result of the region. The sample points included in the resultant grown same color region are used as the estimation result of the region. 
     More specifically, for example, as shown in  FIG. 30A , pixel values of pixels adjacent to the tracking point in eight directions are read out. That is, pixel values of pixels adjacent to the tracking point in the upward direction, upper right direction, right direction, lower right direction, downward direction, lower left direction, left direction, and upper left direction are read out. The difference between the readout pixel value and the pixel value of a tracking point  321  is computed. Thereafter, it is determined whether the computed difference is greater than or equal to the threshold value THimg. In an example shown in  FIG. 30A , each of the differences between the pixel values of the pixels in the directions indicated by arrows (i.e., the pixels in the upward direction, upper right direction, downward direction, left direction, and upper left direction) and the tracking point  321  is less than the threshold value THimg. In contrast, each of the differences between the pixel values of the pixels in the directions not indicated by arrows (i.e., the pixels in the right direction, lower right direction, and lower left direction) and the tracking point  321  is greater than or equal to the threshold value THimg. 
     In this case, as shown in  FIG. 30B , the pixels having the difference less than the threshold value THimg (the pixels indicated by arrows from the tracking point  321 ) are registered as pixels  322  in the same color region including the tracking point  321 . The same process is performed for the pixels  322  registered in the same color region. In an example shown in  FIG. 30B , the difference between the pixel value of the pixel  322  indicated by a white circle at the upper left and the pixel value of the pixel adjacent to the pixel  322  (except for the pixel already determined to be the same color region) is computed. It is then determined whether the difference is greater than or equal to the threshold value THimg. In the example shown in  FIG. 30B , the determination process of the same color region for the pixels in the right direction, lower right direction, and downward direction have been already executed. Accordingly, the differences in the upward direction, upper right direction, lower left direction, left direction, and upper left direction are computed. Also, in this example, the differences in the upward direction, upper right direction, and upper left direction are less than the threshold value THimg. As shown in  FIG. 30C , the pixels in these directions are registered as pixels of the same color region including the tracking point  321 . 
     Such a process is sequentially repeated. Thus, as shown in  FIG. 31 , of the sample points, the points included in the same color region  331  are estimated to be the points of the object including the tracking point  321 . 
     After the region estimation process shown in  FIG. 29  (step S 61  shown in  FIG. 10 ) is completed, a transfer candidate extraction process is executed at step S 62  shown in  FIG. 10  by the transfer candidate extraction unit  42  shown in  FIG. 28 . This transfer candidate extraction process is illustrated by a flow chart shown in  FIG. 32 . 
     That is, at step S 231 , the transfer candidate extraction unit  42  determines all the points that are estimated to be the region (the same color region) to be the transfer candidates without change. The transfer candidate extraction unit  42  then delivers the transfer candidates to the transfer candidate storage unit  16 , which stores the transfer candidates. 
     In the region-estimation related processing unit  15  shown in  FIG. 28 , a template generation process performed by the template generation unit  43  shown in  FIG. 28  at step S 63  shown in  FIG. 10  after the transfer candidate extraction process shown in  FIG. 32  (step S 62  shown in  FIG. 10 ) is completed is the same as the process shown in  FIG. 24 . Thus, description is not repeated. 
     However, in this case, the same color region including the tracking point may be directly determined to be the template region. 
     The exception processing at step S 2  performed after the above-described normal processing at step S 1  shown in  FIG. 2  is completed is described in detail next with reference to a flow chart shown in  FIG. 33 . As noted above, this processing is performed when it is determined at step S 24  shown in  FIG. 6  that the motion of the tracking point cannot be estimated and when it is determined at step S 28  that a transfer candidate to which the tracking point is transferred cannot be selected. 
     At step S 301 , the control unit  19  performs an initialization process of the exception processing. The details of this process are illustrated by a flow chart shown in  FIG. 34 . 
     At step S 321 , the control unit  19  determines whether a scene change occurs when the control unit  19  cannot track the tracking point (when the control unit  19  cannot estimate the motion of the tracking point and cannot select a transfer candidate to which the tracking point is transferred). The scene change detection unit  13  monitors whether a scene change occurs on the basis of the estimation result from the motion estimation unit  12  at all times. The control unit  19  makes the determination at step S 321  on the basis of the detection result from the scene change detection unit  13 . The detailed process of the scene change detection unit  13  is described below with reference to  FIGS. 50 and 51 . 
     If the scene change occurs, the control unit  19  estimates that the occurrence of the scene change prevents the tracking of the tracking point. Thus, at step S 322 , the control unit  19  sets the mode to a scene change. In contrast, if it is determined at step S 321  that the scene change does not occur, the control unit  19  sets the mode to another mode at step S 323 . 
     After the process at step S 322  or S 323  is completed, the template matching unit  11 , at step S 324 , executes a process for selecting the temporally oldest template. More specifically, as shown in  FIG. 35 , for example, when the frame n is changed to the frame n+1 and the exception processing is performed, the template matching unit  11  selects a template generated for a frame n−m+1, which is the temporally oldest template among m templates generated for the frame n−m+1 to the frame n stored in the template storage unit  18 . 
     Thus, the reason why, in place of the template immediately before the transition to the exception processing (the template generated for the frame n in the example shown in  FIG. 35 ), the template at some time ahead of the transition is used is that when transition to the exception processing occurs due to, for example, occlusion of the object to be tracked, most of the object is already hidden immediately before the transition occurs, and therefore, the template at that time is highly likely not to capture a sufficiently large image of the object. Accordingly, by selecting a template at a time slightly ahead of the transition, reliable tracking can be provided. 
     At step S 325 , the template matching unit  11  executes a process for determining a template search area. For example, the template search area is determined so that the position of the tracking point immediately before the transition to the exception processing becomes a center of the template search area. 
     That is, as shown in  FIG. 36 , suppose that the right eye  502  of the face  504  of a subject in the frame n is specified as the tracking point  501 . In the frame n+1, the ball  521  coming from the left covers the face  504  including the tracking point  501 . In the frame n+2, the tracking point  501  reappears. In this case, the area at the center of which is the tracking point  501  (included in a template region  311 ) is determined to be a template search area  312 . 
     At step S 326 , the template matching unit  11  resets the number of passed frames and the number of scene changes after the transition to the exception processing to zero. The number of passed frames and the number of scene changes are used in a continuation determination process at step S 305  shown in  FIG. 33  (at steps S 361 , S 363 , S 365 , and S 367  shown in  FIG. 37 ), which is described below. 
     As described above, the initialization process of the exception processing is completed. Thereafter, at step S 302  shown in  FIG. 33 , the control unit  19  executes a process to wait for the next frame. At step S 303 , the template matching unit  11  executes a template matching process inside the template search area. At step S 304 , the template matching unit  11  determines whether the return to the normal processing is possible. 
     More specifically, in the template matching process, the sum of the absolute values of the differences between a template in a frame several frames ahead (pixels in the template region  311  shown in  FIG. 36 ) and pixels to be matched in the template search area is computed. More precisely, the sum of absolute values of differences between pixels of a predetermined block in the template region  311  and pixels of a predetermined block in the template search area is computed. The position of the block is sequentially moved in the template region  311  and the sum of absolute values of differences is added and is defined as the value at the position of the template. Thereafter, a position having a minimum sum of absolute differences and the value of the position when the template is sequentially moved in the template search area are searched for. At step S 304 , the minimum sum of absolute values of differences is compared with a predetermined threshold value. If the minimum sum of absolute differences is less than or equal to the threshold value, it is determined that the image including the tracking point (included in the template) reappears, and therefore, it is determined that the return to the normal processing is possible. The process then returns to the normal processing at step S 1  shown in  FIG. 2 . 
     Subsequently, as described above, at step S 41  shown in  FIG. 7 , it is determined that the process has returned to the normal processing. At step S 44 , the position having the minimum sum of absolute differences is considered to be the position at which the template is matched. Thereafter, the tracking point and the region estimation range are determined on the basis of the positional relationship among the matched position, the position of the template stored in association with the template, and the region estimation range of the tracking point. That is, as described above in relation to  FIG. 27 , the region estimation range  302  is determined on the basis of the vectors  306  and  307  with respect to the tracking point  305 . 
     However, when a method in which the region estimation range is not used is employed in the region estimation process at step S 61  shown in  FIG. 10  (e.g., the region estimation process shown in  FIG. 29 ), the region estimation range is not determined. 
     To determine, at step S 304  shown in  FIG. 33 , whether the return to the normal processing is possible, a value obtained by dividing the minimum sum of absolute differences by the activity of the template may be compared with a threshold value. In this case, the value computed by an activity computing unit  602  at step S 532  shown in  FIG. 49  can be used as the activity. 
     Alternatively, to determine whether the return to the normal processing is possible, a value obtained by dividing the minimum sum of absolute differences by the minimum sum of absolute differences one frame ahead may be compared with a threshold value. In this case, the need for computing the activity is eliminated. 
     That is, at step S 304 , the correlation between the template and the template search area is computed. The determination is made on the basis of the comparison between the correlation and the threshold value. 
     If, at step S 304 , it is determined that the return to the normal processing is not possible, the process proceeds to step S 305 , where the continuation determination process is executed. The continuation determination process is described in detail below with reference to a flow chart shown in  FIG. 37 . In this process, it is determined whether the tracking process can be continued or not. 
     At step S 306 , the control unit  19  determines whether to continue to track the tracking point on the basis of the result of the continuation determination process (on the basis of flags set at step S 366  or S 368  shown in  FIG. 37 , which is described below). If the tracking process of the tracking point can be continued, the process returns to step S 302  and the processes subsequent to step S 302  are repeated. That is, the process to wait until the tracking point reappears is repeatedly executed. 
     However, if, at step S 306 , it is determined that the tracking process of the tracking point cannot be continued (i.e., it is determined at step S 365  shown in  FIG. 37  that the number of passed frames after the tracking point disappeared is greater than or equal to a threshold value THfr or it is determined at step S 367  that the number of scene changes is greater than or equal to a threshold value THsc), it is determined that the tracking process cannot be executed. Thus, the tracking process is completed. 
       FIG. 37  illustrates the continuation determination process at step S 305  shown in  FIG. 33  in detail. At step S 361 , the control unit  19  executes a process to increment the number of passed frames serving as a variable by one. The number of passed frames is reset to zero in advance in the initialization process (at step S 326  shown in  FIG. 34 ) of the exception processing at step S 301  shown in  FIG. 33 . 
     At step S 362 , the control unit  19  determines whether a scene change occurs or not. Since the scene change detection unit  13  executes a process to detect a scene change at all times, it can be determined whether a scene change occurs or not on the basis of the detection result of the scene change detection unit  13 . If a scene change occurs, the process proceeds to step S 363 , where the control unit  19  increments the number of scene changes serving as a variable. The number of scene changes is also reset to zero in advance in the initialization process at step S 326  shown in  FIG. 34 . If a scene change does not occurs in the case where the normal processing is transferred to the exception processing, the process at step S 363  is skipped. 
     Subsequently, at step S 364 , the control unit  19  determines whether the mode currently being set is a scene change mode or not. This mode is set at step S 322  or S 323  shown in  FIG. 34 . If the mode currently being set is a scene change mode, the process proceeds to step S 367 , where the control unit  19  determines whether the number of scene changes is less than the predetermined threshold value THsc. If the number of scene changes is less than the predetermined threshold value THsc, the process proceeds to step S 366 , where the control unit  19  sets a flag indicating that the continuation is possible. If the number of scene changes is greater than or equal to the predetermined threshold value THsc, the process proceeds to step S 368 , where the control unit  19  sets a flag indicating that the continuation is not possible. 
     In contrast, if, at step S 364 , it is determined that the mode currently being set is not a scene change mode (if it is determined that the mode is another mode), the process proceeds to step S 365 , where the control unit  19  determines whether the number of passed frames is less than the predetermined threshold value THfr. The number of passed frames is also reset to zero in advance in the initialization process at step S 326  of the exception processing shown in  FIG. 32 . If it is determined that the number of passed frames is less than the predetermined threshold value THfr, the flag indicating that the continuation is possible is set at step S 366 . However, if it is determined that the number of passed frames is greater than or equal to the predetermined threshold value THfr, the flag indicating that the continuation is not possible is set at step S 368 . 
     As described above, if the number of scene changes in the template matching process is greater than or equal to the threshold value THsc or if the number of passed frames is greater than or equal to the threshold value THfr, it is determined that the execution of a further tracking process is impossible. 
     If the mode is another mode, it may be determined whether the continuation is possible or not while taking into account the condition that the number of scene changes is zero. 
     In the foregoing description, the process is executed on a frame basis of the image and all the frames are used for the process. However, the process may be executed on a field basis. In addition, in place of using all the frames or all the fields, frames or fields extracted by thinning out frames or fields in predetermined intervals may be used for the process. 
     Furthermore, in the foregoing description, a destination point in the estimated region is used as the transfer candidate, a point in the estimated region can be directly used. In this case, the normal processing at step S 1  shown in  FIG. 2  is changed to the process shown in  FIG. 38  in place of the process shown in  FIG. 6 . 
     The process from step S 401  to step S 410  shown in FIG.  38  is basically the same as the process from step S 21  to step S 29  shown in  FIG. 6 . However, it differs in that the region estimation related process at step S 403  is inserted next to the process to wait for the next frame at step S 402  shown in  FIG. 38 , which corresponds to step S 22  shown in  FIG. 6 , and the update process of the region estimation range at step S 407  is executed in place of the region estimation related process at step S 26  shown in  FIG. 6 . The other processes are the same as those in  FIG. 6 , and therefore, the descriptions are not repeated. 
     The detailed region estimation related process at step S 403  shown in  FIG. 38  is the same as that described in relation to  FIG. 10 . The update process of the region estimation range at step S 407  is the same as that described in relation to  FIG. 16 . 
     When the normal processing is executed according to the flow chart shown in  FIG. 38 , the region estimation process (the region estimation process at step S 61  shown in  FIG. 10 ) of the region estimation related process at step S 403  (the region estimation related process shown in  FIG. 10 ) is illustrated by the flow chart shown in  FIG. 39 . 
     The process from step S 431  through step S 435  is basically the same as the process from step S 81  to step S 86  shown in  FIG. 11 . However, the update process of the region estimation range at step S 86  shown in  FIG. 11  is removed from the process shown in  FIG. 39 . The other processes are the same as those in  FIG. 11 . That is, since the update process of the region estimation range is executed at step S 407  shown in  FIG. 38 , it is not necessary in the region estimation process shown in  FIG. 39 . 
     Furthermore, when the normal processing shown in  FIG. 38  is executed, the transfer candidate extraction process (the transfer candidate extraction process at step S 62  shown in  FIG. 10 ) of the region estimation related process (the region estimation related process shown in  FIG. 10 ) at step S 403  is illustrated in  FIG. 40 . The process at step S 451  is the same as the transfer candidate extraction process at step S 231  shown in  FIG. 32 . 
     As described above, the difference between the process when the normal processing is executed according to the flow chart shown in  FIG. 38  and the process when the normal processing is executed according to the flow chart shown in  FIG. 6  is illustrated in  FIGS. 41 and 42 . 
     When the normal processing is executed according to the flow chart shown in  FIG. 6  and when, as shown in  FIG. 41 , the region  82  is composed of points  551  indicated by black squares in the region estimation range  81  in the frame n, points  552  at positions to which the points  551  in the region  82  in the previous frame n are shifted on the basis of motion vectors  553  are determined to be the transfer candidates in the frame n+1 (process at step S 161  in  FIG. 23 ). 
     The motion vector  553  of each point  551  is sometimes equal to the motion vector of the full-screen motion. However, the estimated motions of the points are slightly different from each other depending on the precision involving in determining whether the motion of each point is equal to the full-screen motion. For example, if it is determined that motions having one-dot difference are the same in the horizontal direction and the vertical direction, the motion of (0, 0) includes the motion of (−1, 1) and the motion of (1, 0). In this case, even when the full-screen motion is (0, 0), each point  551  having the motion of (−1, 1) or (1, 0) is shifted by the amount of the motion. Instead of directly using the destination point as a transfer candidate, the closest point among the sample points obtained in advance may be determined to be the transfer candidate. Off course, to reduce the processing load, each point  551  may be shifted by the amount of the full-screen motion. 
     In contrast, when the normal processing is executed according to the flow chart shown in  FIG. 38 , points  561  inside the region estimation range  81  in the frame n is determined to be the transfer candidates, as shown in  FIG. 42 . 
     An exemplary configuration of the motion estimation unit  12  shown in  FIG. 1  is described next with reference to  FIG. 43 . The motion estimation unit  12  includes a motion vector detection unit  606 - 1  and a motion vector accuracy computing unit  606 - 2 . In this embodiment, an input image is delivered to an evaluation value computing unit  601 , the activity computing unit  602 , and the motion vector detection unit  606 - 1 . 
     The motion vector detection unit  606 - 1  detects a motion vector from an input image and delivers the detected motion vector and the input image to the motion vector accuracy computing unit  606 - 2 . If the input image already contains a motion vector, the motion vector detection unit  606 - 1  separates the image data from the motion vector and delivers the image data and the motion vector to the motion vector accuracy computing unit  606 - 2 . If the input data and the motion vector are separately input, the need for the motion vector detection unit  606 - 1  can be eliminated. 
     The motion vector accuracy computing unit  606 - 2  computes the accuracy of the corresponding motion vector on the basis of the input image (image data) (hereinafter referred to as “motion vector accuracy”) and outputs the obtained accuracy together with the motion vector delivered from the motion vector detection unit  606 - 1 . 
     In this embodiment, the motion vector accuracy computing unit  606 - 2  includes the evaluation value computing unit  601 , the activity computing unit  602 , and a computing unit  606 - 3 . The computing unit  606 - 3  includes a threshold-value determination unit  603 , a normalization processing unit  604 , and the integration processing unit  605 . 
     The motion vector delivered from the motion vector detection unit  606 - 1  shown in  FIG. 43  is input to the evaluation value computing unit  601 . The input image (image data) is input to the evaluation value computing unit  601  and the activity computing unit  602 . 
     The evaluation value computing unit  601  computes the evaluation value of the input image and delivers the evaluation value to the normalization processing unit  604 . The activity computing unit  602  computes the activity of the input image and delivers the activity to the threshold-value determination unit  603  and the normalization processing unit  604  of the computing unit  606 - 3 . 
     The normalization processing unit  604  normalizes the evaluation value delivered from the evaluation value computing unit  601  on the basis of the activity delivered from the activity computing unit  602  and delivers the obtained value to the integration processing unit  605 . The threshold-value determination unit  603  compares the activity delivered from the activity computing unit  602  with a predetermined threshold value and delivers the determination result to the integration processing unit  605 . The integration processing unit  605  computes the motion vector accuracy on the basis of the normalization information delivered from the normalization processing unit  604  and the determination result delivered from the threshold-value determination unit  603  so as to compute the motion vector accuracy. The integration processing unit  605  then outputs the obtained motion vector accuracy to an apparatus. At that time, the integration processing unit  605  may also output the motion vector delivered from the motion vector detection unit  606 - 1 . 
     The motion computing process performed by the motion estimation unit  12  is described in detail next with reference to a flow chart shown in  FIG. 44 . The motion vector detection unit  606 - 1  acquires an input image at step S 501 , divides the frame of the input image into predetermined blocks at step S 502 , and compares the frame with the temporally subsequent (or preceding) frame so as to detect a motion vector at step  503 . More specifically, the motion vector is detected by using a block matching method. The detected motion vector is delivered to the evaluation value computing unit  601 . 
     This process is described next with reference to  FIGS. 45 to 48 . That is, at step S 501  shown in  FIG. 44 , for example, as shown in  FIG. 45 , N frames F 1  (a first frame) to F N  (a Nth frame) are sequentially acquired. At step S 502 , an image in one frame is divided into square blocks, each having sides of 2L+1 pixels. Here, let any block in a frame F n  be a block B p  and, as shown in  FIG. 46 , let the center coordinates (pixel) of the block B p  be a point P(X p , Y p ). 
     At step S 503 , for example, as shown in  FIG. 47 , in a frame F n+1 , which is a frame next to the frame F n , the block B p  scans a predetermined scanning area in the frame F n+1  so as to examine the position that minimizes the sum of absolute differences of the corresponding pixels. Thus, the block (block B q ) located at the position that minimizes the sum of absolute differences of the corresponding pixels is detected. The center point Q(X q , Y q ) of the detected block is determined to be a point corresponding to the point P(X p , Y p ) of the block B p . 
     As shown in  FIG. 48 , a line (arrow) between the center point P(X p , Y p ) of the block B p  and the center point Q(X q , Y q ) of the block B q  is detected as a motion vector V(vx, vy). That is, the motion vector V(vx, vy) is computed according to the following equation:
 
 V ( vx,vy )= Q ( X   q   ,Y   q )− P ( X   p   ,Y   p )  (1)
 
     At step S 504  shown in  FIG. 44 , the attribute information storage unit  22  executes a motion vector accuracy computing process. This process is described in detail below with reference to  FIG. 49 . The motion vector accuracy is computed as a quantitative value by this process. 
     At step S 505 , the motion vector accuracy computing unit  606 - 2  determines whether the computation of motion vector accuracy is completed for all the blocks in one frame. 
     If, at step S 505 , the motion vector accuracy computing unit  606 - 2  determines that the computation of motion vector accuracy is not completed for all the blocks in the frame, the process returns to step S 504  and the processes subsequent to step S 504  are repeatedly executed. If the motion vector accuracy computing unit  606 - 2  determines that the computation of motion vector accuracy is completed for all the blocks, the process for that frame is completed. The above-described process is executed for each frame. 
     The motion vector accuracy computing process at step S 504  shown in  FIG. 44  is described in detail next with reference to a flow chart shown in  FIG. 49 . At step S 531 , the evaluation value computing unit  601  computes an evaluation value Eval(P, Q, i, j) according to the following equation:
 
Eval( P,Q,i,j )=ΣΣ| F   j ( X   q   +x,Y   q   +y )− Fi ( X   p   +x,Y   p   +y )|  (2)
 
     The total sum ΣΣ in equation (2) is computed for x in the range from −L to L and for y in the range from −L to L. That is, for simplicity, suppose, as shown in  FIG. 50 , the block B p  and the block B q  have the sides of 5 (=2L+1=2×2+1) pixels. Then, the difference between the pixel value of a pixel  71  located at the coordinates (point P 1 (X p −2, Y p −2)) at the upper left corner of the block B p  in the frame F n  and the pixel value of a pixel  881  located at the coordinates (point Q 1 (X q −2, Y q −2)) of the block B q  in the frame F n+1  corresponding to the pixel  771  is computed. Similarly, the difference between the pixel value of each pixel located between the point P 1 (X p −2, Y p −2) and P 25 (X p +2, Y p +2) and the pixel value of the corresponding pixel of the block B q  located between Q 1 (X q −2, Y q −2) to Q 25 (X q +2, Y q +2) is computed. When L=2, 25 differences are obtained and the total sum of the absolute differences is computed. 
     The number of pixels (pixels of interest) located at P(X p , Y p ), which is the center coordinates of the above-described block B p  in the frame F n , and the number of the pixels (the corresponding pixels) located at Q (X q , Y q ) which is the center coordinates of the block B q  in the frame F n+1  and which corresponds to the center point of the block B p  may be at least one. However, when a plurality of the pixels are used, the numbers are required to be the same. 
     This evaluation value indicates the evaluation value between a block at the center of which is each point in one frame and a block at the center of which is that point in the other frame (i.e., the evaluation value of the motion vector). As the evaluation value is closer to zero, the blocks become more similar to each other. It is noted that, in equation (2), F i  and F j  represent temporally different frames. In the foregoing description, F n  corresponds to F i  and F n+1  corresponds to F j . In equation (2), although the sum of absolute differences serves as the evaluation value, the sum of squared differences may be determined to be the evaluation value. 
     In place of the block matching method, a gradient method or a vector detection method can be employed. 
     The evaluation value computing unit  601  delivers the generated evaluation value to the normalization processing unit  604 . 
     At step S 532 , the activity computing unit  602  computes the activity from the input image. The activity refers to the feature quantity that indicates the complexity of an image. As shown in  FIGS. 51 and 52 , the average of absolute sum of differences between a pixel of interest Y(x, y) for each pixel and the adjacent 8 pixels, that is, adjacent pixels Y(x−1, y−1), Y(x, y−1), Y(x+1, y−1), Y(x+1, y), Y(x+1, y+1), Y(x, y+1), Y(x−1, y+1), and Y(x−1, y), is computed as the activity of the pixel of interest according to the following equation: 
     
       
         
           
             
               
                 
                   
                     Activity 
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     In an example shown in  FIG. 52 , the value of the pixel of interest Y(x, y), which is located at the center of 3-by-3 pixels, is  110 . The values of the eight pixels adjacent to the pixel of interest Y(x, y) (adjacent pixels Y(x−1, y−1), Y(x, y−1), Y(x+1, y−1), Y(x+1, y), Y(x+1, y+1), Y(x, y+1), Y(x−1, y+1), and Y(x−1, y)) are 80, 70, 75, 100, 100, 100, 80, and 80, respectively. Thus, the activity is expressed by the following equation:
 
Activity( x,y )={|80−110|+|70−110|+|75−110|++|100−110|+|100−110|+|80−110|+|80−110|}/8=24.375
 
     When the motion vector accuracy is computed on a pixel basis, this activity is directly used for computing the motion vector accuracy. When the motion vector accuracy is computed on a block basis (a block including a plurality of pixels), the activity of a block is further computed. 
     To compute the motion vector accuracy on a block basis, for example, as shown in  FIG. 53A , for the block BP having sides of 5 (=2L+1=2×2+1), a pixel  771  included at the center of an activity computing area  851   a  is defined as a pixel of interest. Thereafter, the activity is computed using the value of the pixel  771  and the values of eight pixels adjacent to the pixel  771 . 
     Additionally, as shown in  FIGS. 53B  to F, pixels in the block B p  are sequentially scanned to compute the activity of the pixel of interest with respect to the adjacent pixels included in each of the activity computing areas  851   b  to  851   f . The total sum of the activities computed for all the pixels in the block B p  is defined as the activity of block for the block B p . 
     Accordingly, the total sum of the activities computed for all the pixels in the block expressed by the following equation is defined as the activity of a block (the block activity) Blockactivity(i, j):
 
Block a ctivity( i,j )=ΣΣ|Activity( x,y )|  (4)
 
     The total sum given by equation (4) is computed for x in the range from −L to L and for y in the range from −L to L. “i” and “j” in equation (4) represent the center position of a block, thus being different from i and j in equation (3). 
     It is noted that the variance of the block, the dynamic range, or other values for indicating the variation of pixel value in the spatial direction can be used for the activity. 
     At step S 534 , the threshold-value determination unit  603  determines whether the block activity computed by the activity computing unit  602  at step S 532  is greater than a predetermined threshold value (a threshold value THa, which is described below with reference to  FIG. 53 ). This process is described in detail below with reference to a flow chart shown in  FIG. 54 . In this process, a flag indicating whether the block activity is greater than the threshold value THa is set. 
     At step S 534 , the normalization processing unit  604  executes a normalization process. This process is described in detail below with reference to  FIG. 56 . In this process, the motion vector accuracy is computed on the basis of the evaluation value computed at step S 31 , the block activity computed at step S 532 , and a threshold value (the gradient of a line  903 , which is described below with reference to  FIG. 55 ). 
     At step S 535 , the integration processing unit  605  executes an integrating process. This process is described in detail below with reference to  FIG. 57 . In this process, the motion vector accuracy output to an apparatus (not shown) is determined on the basis of the flag set at step S 533  (step S 552  or step S 553  shown in  FIG. 54 ). 
     A threshold process at step S 533  shown in  FIG. 49  is described in detail with reference to  FIG. 54 . At step S 551 , the threshold-value determination unit  603  determines whether the computed block activity is greater than the threshold value THa on the basis of the result of the process at step S 532  shown in  FIG. 49 . 
     More specifically, the experimental results indicate that the block activity has a relation with the evaluation value using the motion vector as a parameter, as shown in  FIG. 55 . In  FIG. 55 , the abscissa represents the block activity blockactivity(i, j) and the ordinate represents the evaluation value Eval. If a motion is correctly detected (if a correct motion vector is given), the values of the block activity and the values of the evaluation value are distributed in a lower region R 1  below a curve  901 . In contrast, if an erroneous motion (wrong motion vector) is given, the values of the block activity and the values of the evaluation value are distributed in a left region R 2  of a curve  902  (the values are rarely dispersed in an area other than the region R 2  above the curve  902  and the region R 1  below the curve  901 ). The curve  901  crosses the curve  902  at a point P. The value of the block activity at the point P is defined as the threshold value THa. The threshold value THa indicates that, if the value of the block activity is less than the threshold value THa, there is a possibility that the corresponding motion vector is incorrect (this is described in detail below). The threshold-value determination unit  603  outputs a flag indicating whether the value of the block activity input from the activity computing unit  602  is greater than the threshold value THa to the integration processing unit  605 . 
     If, at step S 551 , it is determined that the block activity is greater than the threshold value THa (the corresponding motion vector is highly likely to be correct), the process proceeds to step S 552 . At step S 552 , the threshold-value determination unit  603  sets the flag indicating that the block activity is greater than the threshold value THa. 
     In contrast, if, at step S 551 , it is determined that the block activity is not greater than (i.e., less than) the threshold value THa (there is the possibility that the corresponding motion vector is incorrect), the process proceeds to step S 553 . At step S 553 , the flag indicating that the block activity is not greater than (i.e., less than) the threshold value THa is set. 
     Thereafter, the threshold-value determination unit  603  outputs the flag indicating whether the input block activity is greater than the threshold value to the integration processing unit  605 . 
     The normalization process at step S 534  shown in  FIG. 49  is described in detail next with reference to a flow chart shown in  FIG. 56 . At step S 571 , the normalization processing unit  604  computes the motion vector accuracy VC on the basis of the evaluation value computed at step S 531 , the block activity computed at step S 532 , and the predetermined threshold value (the gradient of the line  903  shown in  FIG. 55 ) according to the following equation:
 
 VC= 1−evaluation value/block activity  (5)
 
     In the motion vector accuracy VC, the value obtained by dividing the evaluation value by the block activity determines a position in the graph shown in  FIG. 55  and indicates whether the position is located in the lower region or upper region with respect to the line  903  between the original point O and the point P having a gradient of 1. That is, the gradient of the line  903  is 1. If the value obtained by dividing the evaluation value by the block activity is greater than 1, the point corresponding to this value is distributed in the region above the line  903 . It means that, as the motion vector accuracy VC obtained by subtracting 1 from this value is smaller (greater for the negative value), the possibility that the corresponding point is distributed in the region R 2  increases. 
     In contrast, if the value obtained by dividing the evaluation value by the block activity is less than 1, the point corresponding to this value is distributed in the region below the line  903 . It means that, as the motion vector accuracy VC is larger (closer to 0), the possibility that the corresponding point is distributed in the region R 1  increases. The normalization processing unit  604  outputs the motion vector accuracy VC obtained in this manner to the integration processing unit  605 . 
     At step S 572 , the normalization processing unit  604  determines whether the motion vector accuracy VC computed according to equation (5) is less than 0 or not (whether the motion vector accuracy VC is negative or not). If the motion vector accuracy VC is greater than or equal to 0, the process of the normalization processing unit  604  proceeds to step S 573 . At step S 573 , the normalization processing unit  604  directly delivers the motion vector accuracy VC computed at step S 571  to the integration processing unit  605 . 
     However, if, at step S 572 , it is determined that the motion vector accuracy VC is less than 0 (the motion vector accuracy VC is negative), the process proceeds to step S 574 . At step S 574 , the normalization processing unit  604  sets the motion vector accuracy VC to a fixed value of 0 and delivers the motion vector accuracy VC to the integration processing unit  605 . 
     Thus, if there is the possibility that the motion vector is incorrect (the motion vector is a wrong vector) (i.e., the motion vector accuracy VC is negative), the motion vector accuracy is set to 0. 
     The integration process at step S 535  shown in  FIG. 49  is described in detail next with reference to a flow chart shown in  FIG. 57 . 
     At step S 591 , the integration processing unit  605  determines whether the block activity is less than or equal to the threshold value THa. This determination is made on the basis of the flag delivered from the threshold-value determination unit  603 . If the block activity is greater than the threshold value THa, the integration processing unit  605 , at step S 592 , directly outputs the motion vector accuracy VC computed by the normalization processing unit  604  together with the motion vector. 
     In contrast, if it is determined that the block activity is less than or equal to the threshold value THa, the motion vector accuracy VC computed by the normalization processing unit  604  is set to 0 and is output at step S 593 . 
     This is because, even when the motion vector accuracy VC computed by the normalization processing unit  604  is positive, there is a possibility that the correct motion vector is not obtained if the block activity value is less than the threshold value THa. That is, as shown in  FIG. 55 , between the original point O and the point P, a curve  202  extends downward past the curve  901  (downward past the line  903 ). In an area R 3  enclosed by the curve  901  and the curve  902  where the block activity is less than the threshold value THa, the value obtained by dividing the evaluation value by the block activity is distributed in both regions R 1  and R 2 , and therefore, it is highly likely that the correct motion vector will not be obtained. Accordingly, in such a distribution, the process is executed based on the assumption that the motion vector accuracy is low. Thus, when the motion vector accuracy VC is negative and even when the motion vector accuracy VC is positive, the motion vector accuracy VC is set to 0 if the threshold value THa is less than the threshold value THa. This design allows the positive motion vector accuracy VC to reliably represent that the correct motion vector is obtained. Furthermore, as the value of the motion vector accuracy VC increases, the possibility that the correct motion vector is obtained increases (the possibility that the distribution is included in the region R 1  increases). 
     This result matches empirical laws suggesting that, in general, it is difficult to obtain a reliable motion vector in an area where the luminance change is low (area where the activity is low). 
     Thus, the motion vector accuracy is computed. Consequently, the motion vector accuracy can be represented by a quantitative value, and therefore, a reliable motion vector can be detected. While the process has been described with reference to an image of a frame, the process can be applied to an image of a field. 
       FIG. 58  illustrates an exemplary configuration of the background motion estimation unit  14  shown in  FIG. 1 . In this example, the background motion estimation unit  14  includes a frequency distribution computing unit  1051  and a background motion determination unit  1052 . 
     The frequency distribution computing unit  1051  computes the frequency distribution of motion vectors. It is noted weighting is applied to the frequency by using the motion vector accuracy VC delivered from the motion estimation unit  12  so as to weight a motion that is likely to be reliable. The background motion determination unit  1052  determines a motion having a maximum frequency to be the background motion on the basis of the frequency distribution computed by the frequency distribution computing unit  1051 . The background motion determination unit  1052  then outputs the motion to the region-estimation related processing unit  15 . 
     A background motion estimation process performed by the background motion estimation unit  14  is now herein described with reference to  FIG. 59 . 
     At step S 651 , the frequency distribution computing unit  1051  computes the frequency distribution of motions. More specifically, when an x coordinate and a y coordinate of a motion vector serving as a candidate of a background motion are represented in the range of ±16 pixels from a reference point, the frequency distribution computing unit  1051  prepares 1089 (=16×2+1)×(16×2+1)) boxes, that is, boxes corresponding to the coordinates of the possible points of the motion vector. When a motion vector occurs, the frequency distribution computing unit  1051  increments the coordinates corresponding to the motion vector by 1. Thus, the frequency distribution of motion vectors can be computed. 
     However, if a value of 1 is added when one motion vector occurs and if the frequency of occurrence of a low-accuracy motion vector is high, that low-accuracy motion vector is possibly determined to be the background motion. Therefore, when a motion vector occurs, the frequency distribution computing unit  1051  does not add a value of 1 to the box (coordinates) corresponding to that motion vector, but adds a value of 1 multiplied by the motion vector accuracy VC (=the value of the motion vector accuracy VC) to the box. The value of the motion vector accuracy VC is normalized to a value in the range of 0 to 1. As this value is closer to 1, the accuracy is higher. Accordingly, the frequency distribution obtained using the above-described method becomes the frequency distribution in which a motion vector is weighted on the basis of the accuracy thereof. Thus, the risk that a low-accuracy motion is determined to be the background motion is reduced. 
     At step S 652 , the frequency distribution computing unit  1051  determines whether it has completed the process to compute the frequency distribution of motions for all the blocks. If an unprocessed block is present, the process returns to step S 651 , where the process at step S 651  is executed for the next block. 
     Thus, the process to compute the frequency distribution of motions is executed for the full screen. If, at step S 652 , it is determined that the process for all the blocks has been completed, the process proceeds to step S 653 . At step S 653 , the background motion determination unit  1052  executes a process to search for a maximum value of the frequency distribution. That is, the background motion determination unit  1052  selects a maximum frequency from among the frequencies computed by the frequency distribution computing unit  1051  and determines the motion vector corresponding to the selected frequency to be the motion vector of background. This motion vector of the background motion is delivered to the region-estimation related processing unit  15  and is used for, for example, determining whether the motion of background is equal to the full-screen motion at step S 104  shown in  FIG. 16  and at step S 131  shown in  FIG. 21 . 
       FIG. 60  illustrates an exemplary configuration of the scene change detection unit  13  shown in  FIG. 1  in detail. In this example, the scene change detection unit  13  includes a motion-vector-accuracy average computing unit  1071  and a threshold determination unit  1072 . 
     The motion-vector-accuracy average computing unit  1071  computes the average of the motion vector accuracy VC delivered from the motion estimation unit  12  for the full screen and outputs the average to the threshold determination unit  1072 . The threshold determination unit compares the average delivered from the motion-vector-accuracy average computing unit  1071  with a predetermined threshold value. The threshold determination unit  1072  then determines whether a scene change occurs on the basis of the comparison result and outputs the determination result to the control unit  19 . 
     The operation of the scene change-detection unit  13  is described next with reference to a flow chart shown in  FIG. 61 . At step S 681 , the motion-vector-accuracy average computing unit  1071  computes the sum of the vector accuracy. More specifically, the motion-vector-accuracy average computing unit  1071  summarizes the values of the motion vector accuracy VC computed for each block output from the integration processing unit  605  of the motion estimation unit  12 . At step S 682 , the motion-vector-accuracy average computing unit  1071  determines whether the process to compute the sum of the motion vector accuracy VC has been completed for all the blocks. If the process has not been completed for all the blocks, the motion-vector-accuracy average computing unit  1071  repeats the process at step S 681 . By repeating this process, the sum of the motion vector accuracy VC for all the blocks in one screen is computed. If, at step S 682 , it is determined that the process to compute the sum of the motion vector accuracy VC for all the blocks in one screen is completed, the process proceeds to step S 683 . At step S 683 , the motion-vector-accuracy average computing unit  1071  executes the process to compute the average of the motion vector accuracy VC. More specifically, the sum of the vector accuracy VC for one screen computed at step S 681  is divided by the number of blocks used for the addition. The resultant value is defined as the average. 
     At step S 684 , the threshold determination unit  1072  compares the average of the motion vector accuracy VC computed by the motion-vector-accuracy average computing unit  1071  at step S 683  with a predetermined threshold value to determine whether the threshold value is less than the average. In general, if a scene change occurs between two frames of a moving image at different times, the corresponding image disappears. Therefore, even though the motion vector is computed, the accuracy of that motion vector is low. Thus, if the average of the motion vector accuracy VC is less than the threshold value, the threshold determination unit  1072 , at step S 685 , turns on a scene change flag. If the average of the motion vector accuracy VC is not less than (i.e., greater than or equal to) the threshold value, the threshold determination unit  1072 , at step S 686 , turns off the scene change flag. The scene change flag that is turned on indicates that a scene change has occurred, whereas the scene change flag that is turned off indicates that a scene change has not occurred. 
     This scene change flag is delivered to the control unit  19  and is used for determining whether a scene change has occurred at step S 321  shown in  FIG. 34  and at step S 362  shown in  FIG. 37 . 
     An image processing apparatus including the above-described object tracking apparatus is described next.  FIG. 62  illustrates an example in which the object tracking apparatus is applied to a television receiver  1700 . A tuner  1701  receives an RF signal, demodulates the RF signal into an image signal and a audio signal, outputs the image signal to an image processing unit  1702 , and outputs the audio signal to an audio processing unit  1707 . 
     The image processing unit  1702  demodulates the image signal input from the tuner  1701 . The image processing unit  1702  then outputs the demodulated image signal to an object tracking unit  1703 , a zoom image generation unit  1704 , and a selection unit  1705 . The object tracking unit  1703  has virtually the same configuration as the above-described object tracking apparatus  1  shown in  FIG. 1 . The object tracking unit  1703  executes a process to track a tracking point of an object specified by a user in the input image. The object tracking unit  1703  outputs the coordinate information about the tracking point to the zoom image generation unit  1704 . The zoom image generation unit  1704  generates a zoom image at the center of which is the tracking point and outputs the zoom image to the selection unit  1705 . The selection unit  1705  selects one of the image delivered from the image processing unit  1702  and the image delivered from the zoom image generation unit  1704  on the basis of a user instruction and outputs the selected image to an image display  1706 , which displays the image. 
     The audio processing unit  1707  demodulates the audio signal input from the tuner  1701  and outputs the demodulated signal to a speaker  1708 . 
     A remote controller  1710  is operated by the user. The remote controller  1710  outputs signals corresponding to the user operations to a control unit  1709 . The control unit includes, for example, a microcomputer and controls all the components in response to the user instruction. A removable medium  1711  includes a semiconductor memory, a magnetic disk, an optical disk, or a magnetooptical disk. The removable medium  1711  is mounted as needed. The removable medium  1711  provides a program and various types of data to the control unit  1709 . 
     The process of the television receiver  1700  is described next with reference to a flow chart shown in  FIG. 63 . 
     At step S 701 , the tuner  1701  receives an RF signal via an antenna (not shown) and demodulates a signal for a channel specified by the user. The tuner  1701  then outputs an image signal to the image processing unit  1702  and outputs an audio signal to the audio processing unit  1707 . The audio signal is demodulated by the audio processing unit  1707  and is output from the speaker  1708 . 
     The image processing unit  1702  demodulates the input image signal and outputs the image signal to the object tracking unit  1703 , the zoom image generation unit  1704 , and the selection unit  1705 . 
     At step S 702 , the object tracking unit  1703  determines whether tracking is enabled by the user. If the object tracking unit  1703  determines that tracking is not enabled, the object tracking unit  1703  skips the processes at steps S 703  and S 704 . At step S 705 , the selection unit  1705  selects one of the image signal delivered from the image processing unit  1702  and the image signal input from the zoom image generation unit  1704  on the basis of a control from the control unit  1709 . In this case, since a user instruction is not received, the control unit  1709  instructs the selection unit  1705  to select the image signal from the image processing unit  1702 . At step S 706 , the image display  1706  displays the image selected by the selection unit  1705 . 
     At step S 707 , the control unit  1709  determines whether the image display process is completed on the basis of a user instruction. That is, to terminate the image display process, the user operates the remote controller  1710  to instruct the control unit  1709  to terminate the image display process. If the control unit  1709  has not received the user instruction, the process returns to step S 701  and the process subsequent to step S 701  is repeatedly executed. 
     Thus, the normal processing to directly display an image corresponding to a signal received by the tuner  1701  is executed. 
     When an image that the user wants to track is displayed on the image display  1706 , the user operates the tuner  1701  to specify the image. When this operation is carried out, the control unit  1709 , at step S 702 , determines that tracking is enabled and controls the object tracking unit  1703 . Under the control of the control unit  1709 , the object tracking unit  1703  starts tracking the tracking point specified by the user. This process is the same as the process performed by the above-described object tracking apparatus  1 . 
     At step S 704 , the zoom image generation unit  1704  generates a zoom image at the center of which is the tracking point tracked by the object tracking unit  1703  and outputs the zoom image to the selection unit  1705 . 
     This zoom process can be executed by using an adaptive classification technique proposed by the present inventor. For example, Japanese Unexamined Patent Application Publication No. 2002-196737 describes a technology in which a 525i signal is converted to a 1080i signal using a coefficient obtained by a pre-training process. This process is virtually the same process to enlarge an image by a factor of 9/4 in both vertical direction and horizontal direction. However, the number of pixels in the image display  1706  is fixed. Accordingly, in order to, for example, generate a 9/4 times larger image, the zoom image generation unit  1704  can generate a zoom image by converting a 525i signal to a 1080i signal and selecting a predetermined number of pixels at the center of which is the tracking point (the number of pixels corresponding to the image display  1706 ). In order to reduce the image, the reverse operation is executed. 
     An image zoomed by any scale factor can be generated on the basis of this principal. 
     If the tracking instruction is received, the selection unit  1705 , at step S 705 , selects the zoom image generated by the zoom image generation unit  1704 . As a result of the selection, the image display  1706 , at step S 706 , displays the zoom image generated by the zoom image generation unit  1704 . 
     Thus, the zoom image at the center of which is the tracking point specified by the user is displayed on the image display  1706 . If the scale factor is set to 1, only the tracking is performed. 
       FIG. 64  illustrates the functional structure of an image processing apparatus  1801  according to the present invention. The image processing apparatus  1801  includes a motion vector detection unit  1821  and a motion vector accuracy computing unit  1822 . 
     The motion vector detection unit  1821  detects a motion vector from an input image and delivers the detected motion vector and the input image to the motion vector accuracy computing unit  1822 . Additionally, when the input image already contains a motion vector, the motion vector detection unit  1821  separates the image data from the motion vector and delivers the image data and the motion vector to the motion vector accuracy computing unit  1822 . If the input data and the motion vector are separately input, the need for the motion vector detection unit  1821  can be eliminated. 
     The motion vector accuracy computing unit  1822  computes the accuracy of the corresponding motion vector on the basis of the input image (image data) (hereinafter referred to as “motion vector accuracy”) and outputs the obtained accuracy to an apparatus (not shown). 
       FIG. 65  illustrates an exemplary configuration of the motion vector accuracy computing unit  1822  shown in  FIG. 64 . In this embodiment, the motion vector accuracy computing unit  1822  includes an evaluation value computing unit  1841 , an activity computing unit  1842 , and a computing unit  1843 . The computing unit  1843  includes a threshold-value determination unit  1851 , a normalization processing unit  1852 , and the integration processing unit  1853 . 
     The motion vector output from the motion vector detection unit  1821  shown in  FIG. 64  is input to the evaluation value computing unit  1841 . The input image (image data) is input to the evaluation value computing unit  1841  and the activity computing unit  1842 . 
     The evaluation value computing unit  1841  computes the evaluation value of the input image and delivers the evaluation value to the normalization processing unit  1852 . The activity computing unit  1842  computes the activity of the input image and delivers the activity to the threshold-value determination unit  1851  and the normalization processing unit  1852  of the computing unit  1843 . 
     The normalization processing unit  1852  normalizes the evaluation value delivered from the evaluation value computing unit  1841  on the basis of the activity delivered from the activity computing unit  1842  and delivers the obtained value to the integration processing unit  1853 . The threshold-value determination unit  1851  compares the activity delivered from the activity computing unit  1842  with a predetermined threshold value and delivers the determination result to the integration processing unit  1853 . The integration processing unit  1853  computes the motion vector accuracy on the basis of the normalization information delivered from the normalization processing unit  1852  and the determination result delivered from the threshold-value determination unit  1851 . The integration processing unit  1853  then outputs the obtained motion vector accuracy to an apparatus (not shown). 
     The motion vector detection unit  1821 , the motion vector accuracy computing unit  1822 , the evaluation value computing unit  1841 , the activity computing unit  1842 , the computing unit  1843 , the threshold-value determination unit  1851 , the normalization processing unit  1852 , and the integration processing unit  1853  have basically the same configuration as those of the above-described motion vector detection unit  606 - 1 , the motion vector accuracy computing unit  606 - 2 , the evaluation value computing unit  601 , the activity computing unit  602 , the computing unit  606 - 3 , the threshold-value determination unit  603 , the normalization processing unit  604 , and the integration processing unit  605  shown in  FIG. 43 , respectively. Therefore, the detailed descriptions thereof are not repeated. 
     The above-described image processing apparatus  1801  can be composed of, for example, a personal computer. 
     In this case, the image processing apparatus  1801  is configured as described in, for example,  FIG. 66 . A central processing unit (CPU)  1931  executes various processing in accordance with a program stored in a read only memory (ROM)  1932  or a program loaded from a storage unit  1939  into a random access memory (RAM)  1933 . The RAM  1933  also stores data needed for the CPU  1931  to execute the various processing as needed. 
     The CPU  1931 , the ROM  1932 , and the RAM  1933  are connected to each other via a bus  1934 . An input/output interface  1935  is also connected to the bus  1934 . 
     The following components are connected to the input/output interface  1935 : an input unit  1936  including, for example, a keyboard and a mouse, a display including, for example, a cathode ray tube (CRT) or a liquid crystal display (LCD), an output unit  1937  including, for example, a speaker, a communications unit  1938  including, for example, a modem or a terminal adaptor, and a storage unit  1939  including, for example, a hard disk. The communications unit  1938  carries out a process to communicate with a different apparatus via a LAN or the Internet (not shown). 
     A drive  1940  is also connected to the input/output interface  1935 . A removable medium  1941  including a magnetic disk, an optical disk, a magnetooptical disk, or a semiconductor memory is mounted in the drive  1940  as needed. A computer program read out of these media is installed in the storage unit  1939  as needed. 
     A encoding unit  2261  according to the present invention is described next with reference to  FIG. 67 . 
     In the encoding unit  2261 , an input image is delivered to the motion vector detection unit  1821 , a motion compensation unit  2272 , and a selection unit  2273  of a motion computing unit  2271 . The motion computing unit  2271  has virtually the same configuration as that of the above-described image processing apparatus  1801  shown in  FIG. 64 . The motion vector detection unit  1821  detects a motion vector from the input image and outputs the detected motion vector to the motion compensation unit  2272  and an additional code generation unit  2275 . Additionally, the motion vector detection unit  1821  outputs the motion vector and the input image to the motion vector accuracy computing unit  1822 . 
     The motion vector accuracy computing unit  1822  computes the motion vector accuracy on the basis of the motion vector input from the motion vector detection unit  1821  and the input image and outputs the computed motion vector accuracy to a control unit  2274 . The control unit  2274  controls the selection unit  2273  and the additional code generation unit  2275  on the basis of the input motion vector accuracy. 
     The motion compensation unit  2272  compensates for the motion on the basis of the delivered input image and the motion vector delivered from the motion vector detection unit  1821  and delivers the motion-compensated image to the selection unit  2273 . The selection unit  2273  selects the input image or the motion-compensated image and outputs the selected image to a pixel value encoding unit  2276  under the control of the control unit  2274 . The pixel value encoding unit  2276  encodes the received image and output to an integrating unit  2277 . 
     The additional code generation unit  2275  generates an additional code that indicates whether the motion of an image of each frame is compensated for under the control of the control unit  2274  and combines the additional code with the motion vector input from the motion vector detection unit  1821 . The additional code generation unit  2275  adds the motion vector accuracy to the image if needed. The additional code generation unit  2275  then outputs the combined image to the integrating unit  2277 . 
     The integrating unit  2277  integrates the code input from the pixel value encoding unit  2276  and the additional code input from the additional code generation unit  2275 , and outputs the integrated code to an apparatus (not shown). 
     The process of the encoding unit  2261  is described next with reference to a flow chart shown in  FIG. 68 . At steps S 821  through S 825 , the image is input and each frame of the image is divided into predetermined blocks. A motion vector is detected on the basis of the divided blocks. The accuracy of each motion vector (the motion vector accuracy) is computed. The same processes are repeated until the motion vector accuracy is detected for all the blocks. 
     Thereafter, at step S 826 , the motion compensation unit  2272  compensates for the motion on the basis of the input image and the motion vector. That is, a difference between images of the consecutive two frames is computed on the basis of the motion vector and a difference image (motion-compensated image) is generated. 
     At step S 827 , under the control of the control unit  2274 , the selection unit  2273  selects one of the input image and the motion-compensated image delivered from the motion compensation unit  2272 . That is, when the motion vector accuracy is sufficiently high, the control unit  2274  instructs the selection unit  2273  to select the motion-compensated image as an image to be encoded. When the motion vector accuracy is not sufficiently high, the control unit  2274  instructs the selection unit  2273  to select the input image. Since one of the input image and the motion-compensated image is selected on the basis of the motion vector accuracy, an image that is motion-compensated on the basis of low reliable accuracy can be prevented from being used. The selection unit  2273  delivers the selected image to the pixel value encoding unit  2276 . 
     At step S 828 , the pixel value encoding unit  2276  encodes the image selected at step S 828  (the input image or the motion-compensated image). 
     At step S 829 , the additional code generation unit  2275  generates an additional code for indicating whether or not an encoded image required for decoding is a motion-compensated image under the control of the control unit  2274 . This additional code can include the motion vector accuracy. 
     At step S 830 , the integrating unit  2277  integrates the image encoded at step S 828  and the additional code generated at step S 829 . The integrating unit  2277  then outputs the integrated image and additional code to an apparatus (not shown). 
     Thus, the image is encoded so that the image that is motion-compensated on the basis of a motion vector that may be incorrect (that may be a wrong vector) can be prevented from being used. Accordingly, the damage of an image caused by motion compensation using an unreliable motion vector can be prevented, and therefore, a high-quality image can be obtained at a decoding time. 
       FIG. 69  illustrates an example in which the present invention is applied to a camera-shake blur correction apparatus  2301 . For example, the camera-shake blur correction apparatus  2301  is applied to a digital video camera. 
     An input image is input to a background motion detection unit  2311  and an output image generation unit  2314 . The background motion detection unit  2311  detects a background motion from the input image and outputs the detected background motion to a displacement accumulation unit  2312 . The configuration of the background motion detection unit  2311  is described in detail below with reference to  FIG. 70 . The displacement accumulation unit  2312  accumulates the amounts of displacement from the input background motion and outputs the accumulated amount of displacement to a camera-shake blur determination unit  2313  and the output image generation unit  2314 . The camera-shake blur determination unit  2313  determines whether the input displacement information corresponds to camera-shake blur on the basis of a predetermined threshold value and outputs the determination result to the output image generation unit  2314 . 
     The output image generation unit  2314  generates an output image from the delivered input image on the basis of the amount of displacement input from the displacement accumulation unit  2312  and the determination result input from the camera-shake blur determination unit  2313 . The output image generation unit  2314  then records the output image on a writable recording medium  315 , such as a hard disk drive (HDD) and a video tape. Additionally, the output image generation unit  2314  outputs the generated image to a display unit  2316  including, for example, a liquid crystal display (LCD), which displays the generated image. 
       FIG. 70  illustrates the configuration of the background motion detection unit  2311  shown in  FIG. 69  in detail. In this configuration, the background motion detection unit  2311  includes a motion computing unit  2321 , a frequency distribution computing unit  2322 , and a background motion determination unit  2323 . The motion computing unit  2321  has a configuration virtually the same as that of the above-described image processing apparatus  1801  shown in  FIG. 63 . 
     The input image is delivered to the motion vector detection unit  1821  of the motion computing unit  2321 . The motion vector detection unit  1821  detects a motion vector from the input image and outputs the detected motion vector and the input image to the motion vector accuracy computing unit  1822 . The motion vector accuracy computing unit  1822  computes the accuracy of the corresponding motion vector (the motion vector accuracy) on the basis of the input motion vector and the input image and delivers the motion vector accuracy to the frequency distribution computing unit  2322 . 
     The frequency distribution computing unit  2322  computes the frequency distribution of motion vectors. It is noted weighting is applied to the frequency by using the motion vector accuracy VC delivered from the motion computing unit  2321  so as to weight a motion that is likely to be reliable. The background motion determination unit  2323  determines a motion having a maximum frequency to be the background motion on the basis of the frequency distribution computed by the frequency distribution computing unit  2322 . 
     The camera-shake blur correction process performed by the camera-shake blur correction apparatus  2301  is described next with reference to a flow chart shown in  FIG. 71 . At steps S 831  through S 834 , the input image is acquired and a frame of the image is divided into predetermined blocks. A motion vector is detected on the basis of the divided blocks using, for example, the block matching method. The accuracy of each motion vector (the motion vector accuracy) is then computed. 
     At step S 835 , the frequency distribution computing unit  2322  computes the frequency distribution of motions. More specifically, when an x coordinate and a y coordinate of a motion vector serving as a candidate of a background motion are represented in the range of ±16 pixels from a reference point, the frequency distribution computing unit  2322  prepares 1089 ((=16×2+1)×(16×2+1)) boxes, that is, boxes corresponding to the coordinates of the possible points of the motion vector. When a motion vector occurs, the frequency distribution computing unit  2322  increments the coordinates corresponding to the motion vector by 1. Thus, the frequency distribution of motion vectors can be computed. 
     However, if a value of 1 is added when one motion vector occurs and if the frequency of occurrence of a low-accuracy motion vector is high, that low-accuracy motion vector is possibly determined to be the background motion. Therefore, when a motion vector occurs, the frequency distribution computing unit  2322  does not add a value of 1 to the box (coordinates) corresponding to that motion vector/but adds a value of 1 multiplied by the motion vector accuracy VC (=the value of the motion vector accuracy VC) to the box. The value of the motion vector accuracy VC is normalized to a value in the range of 0 to 1. As this value is closer to 1, the accuracy is higher. Accordingly, the frequency distribution obtained using the above-described method becomes the frequency distribution in which a motion vector is weighted on the basis of the accuracy thereof. Thus, the risk that a low-accuracy motion is determined to be the background motion is reduced. 
     At step S 836 , the motion vector accuracy computing unit  1822  determines whether it has completed the process to compute the frequency distribution of motions for all the blocks. If the unprocessed block is present, the process returns to step S 834 , where the processes at steps S 834  and S 835  are executed for the next block. 
     After the process to compute the frequency distribution of motions has been executed for the full screen, the process proceeds to step S 837 . At step S 837 , the background motion determination unit  2323  executes a process to search for a maximum value of the frequency distribution. That is, the background motion determination unit  2323  selects a maximum frequency from among the frequencies computed by the frequency distribution computing unit  2322  and determines the motion vector corresponding to the selected frequency to be the motion vector of the background motion. This motion vector of the background motion is delivered to the displacement accumulation unit  2312 . 
     At step S 838 , the displacement accumulation unit  2312  sequentially stores the motion vector representing the background motion for each frame. 
     At step S 839 , the camera-shake blur determination unit  2313  determines whether the displacement (absolute value) of the motion vector representing the background motion is greater than a predetermined threshold value so as to determine whether the input image is blurred due to camera shake. If the displacement is greater than the threshold value, it is determined that the hand vibration occurs. In contrast, if the displacement is less than the threshold value, it is determined that no hand vibration occurs. The camera-shake blur determination unit  2313  delivers the determination result to the output image generation unit  2314 . 
     If, at step S 839 , the camera-shake blur determination unit  2313  determines that the hand vibration occurs, the output image generation unit  2314 , at step S 840 , generates an image that is shifted by the displacement in the opposite direction and outputs the image. Thus, the user can record or view the image in which blurring due to hand vibration is reduced. 
     In contrast, if, at step S 839 , the camera-shake blur determination unit  2313  determines that no hand vibration occurs, the process proceeds to step S 841 , where the output image generation unit  2314  directly outputs the input image. The output image is recorded on a recording medium  2315  and is displayed on the display unit  2316 . 
     Thus, the camera-shake blur is detected and corrected. The use of the motion vector accuracy allows the background motion to be precisely detected, thereby providing an image with little blurring to the user. 
       FIG. 72  illustrates an exemplary accumulating apparatus  2341  according to the present invention. The accumulating apparatus  2341  serving as a hard disk drive (HDD) recorder includes a selection unit  2351 , a recording medium (HDD)  2352 , an index generation unit  2353 , a scene change detection unit  2354 , a control unit  2355 , an index table  2356 , a selection unit  2357 , a display image generation unit  2358 , a total control unit  2359 , and an instruction input unit  2360 . 
     The selection unit  2351  selects one of an image recorded on the recording medium  2352  and an input image under the control of the total control unit  2359  and delivers the selected image to the index generation unit  2353 , the scene change detection unit  2354 , and the selection unit  2357 . An image is recorded on the recording medium  2352  composed of an HDD under the control of the total control unit  2359 . 
     The scene change detection unit  2354  detects a scene change from the delivered image and delivers the detection result to the control unit  2355 . The control unit  2355  controls the index generation unit  2353  and the index table  2356  on the basis of the delivered detection result. 
     The index generation unit  2353  extracts an index image recorded on the recording medium  2352  and additional information (time code, address, etc.) for identifying the position of the index image on the recording medium  2352  and delivers them to the index table  2356  under the control of the control unit  2355 . The index image is a reduced image of the start image of each scene when it is determined that a scene change occurs. 
     The index table  2356  stores the delivered index image and the corresponding additional information. The index table  2356  delivers the additional information corresponding to the stored index image to the total control unit  2359  under the control of the control unit  2355 . 
     The selection unit  2357  selects one of the image delivered from the selection unit  2351  and the index image input from the index table  2356  and outputs the selected image to the display image generation unit  2358  under the control of the total control unit  2359 . The display image generation unit  2358  generates an image in a format that an image display device  2365  can display from the delivered image and output the image to be displayed under the control of the total control unit  2359 . 
     Under the control of a scene change flag output from the scene change detection unit  2354  and under the control of the total control unit  2359 , the control unit  2355  controls the index generation unit  2353  and the index table  2356 . 
     The total control unit  2359  includes, for example, a microcomputer and controls each component. The instruction input unit  2360  includes a variety of buttons and switches, and a remote controller. The instruction input unit  2360  outputs a signal corresponding to the user instruction to the total control unit  2359 . 
       FIG. 73  illustrates an exemplary configuration of the scene change detection unit  2354  shown in  FIG. 72  in detail. In this example, the scene change detection unit  2354  includes a motion computing unit  2371 , a motion-vector-accuracy average computing unit  2372 , and a threshold determination unit  2373 . The motion computing unit  2371  has virtually the same configuration as that of the above-described image processing apparatus  1801  shown in  FIG. 64 . 
     The motion vector detection unit  1821  detects a motion vector from an input image and delivers the detected motion vector and the input image to the motion vector accuracy computing unit  1822 . On the basis of the input motion vector and image, the motion vector accuracy computing unit computes the accuracy of the corresponding motion vector (motion vector accuracy) and outputs the obtained motion vector accuracy to the motion-vector-accuracy average computing unit  2372 . 
     The motion-vector-accuracy average computing unit  2372  computes the average of the motion vector accuracy VC delivered from the motion computing unit  2371  for the full screen and outputs the average to the threshold determination unit  2373 . The threshold determination unit  2373  compares the average delivered from the motion-vector-accuracy average computing unit  2372  with a predetermined threshold value. The threshold determination unit  2373  then determines whether a scene change occurs on the basis of the comparison result and outputs the determination result to the control unit  2355 . 
     The index image generation process executed when the accumulating apparatus  2341  records an image on the recording medium  2352  is described in detail next with reference to a flow chart shown in  FIG. 74 . This process is executed while the input is being recorded on the recording medium  2352 . 
     The processes at steps S 871  to S 874  are the same as the processes at steps S 501  to S 504  described in relation to FIG.  44 , respectively. That is, in these processes, an image is input and the frame of the image is divided into predetermined blocks. A motion vector is detected on the basis of the divided blocks using, for example, the block matching method. The accuracy of each motion vector (the motion vector accuracy) is then computed. 
     At step S 875 , the motion-vector-accuracy average computing unit  2372  computes the sum of the motion vector accuracy of the image input from the selection unit  2351  (the image being recorded on the recording medium  2352 ). More specifically, the motion-vector-accuracy average computing unit  2372  summarizes the values of the motion vector accuracy VC computed for each block output from the integration processing unit  1853  of the motion vector accuracy computing unit  1822  of the motion computing unit  2371 . At step S 876 , the motion vector accuracy computing unit  1822  determines whether the process to compute the sum of the motion vector accuracy VC has been completed for all the blocks. If the process has not been completed for all the blocks, the motion vector accuracy computing unit  1822  repeats the processes at steps S 874  and S 875 . By repeating these processes, the sum of the motion vector accuracy VC for all the blocks in one screen is computed. If, at step S 876 , it is determined that the process to compute the sum of the motion vector accuracy VC for all the blocks in one screen is completed, the process proceeds to step S 877 . At step S 877 , the motion-vector-accuracy average computing unit  2372  executes the process to compute the average of the motion vector accuracy VC. More specifically, the sum of the vector accuracy VC for one screen computed at step S 875  is divided by the number of blocks of the addition. The resultant value is defined as the average. Accordingly, one average is obtained for one screen (one frame). 
     At step S 878 , the threshold determination unit  2373  compares the average of the motion vector accuracy VC computed by the threshold determination unit  2373  at step S 877  with a predetermined threshold value and outputs the comparison result to the control unit  2355 . At step S 879 , the control unit  2355  determines whether the average is less than the threshold value. In general, if a scene change occurs between two consecutive frames of a moving picture, the corresponding image disappears. Therefore, even though the motion vector is computed, the accuracy of that motion vector is low. Thus, if the average of the motion vector accuracy VC is less than the threshold value, the control unit  2355 , at step S 880 , controls the index generation unit  2353  to generate an index image. 
     That is, at step S 881 , under the control of the control unit  2355 , the index generation unit  2353  reduces the size of the image in the start frame of the new scene to generate an index image. When, for example, 3×3 index images are displayed in a screen, the index image is generated by reducing the sizes of the original image into ⅓ in the vertical and horizontal directions. Additionally, at that time, the index generation unit  2353  extracts the additional information (time code, address, etc.) for identifying the recording position of the image of the frame on the recording medium  2352 . 
     At step S 881 , the index generation unit  2353  stores the index image generated at step S 880  and the corresponding additional information in the index table  2356 . 
     If, at step S 879 , it is determined the average of the motion vector accuracy VC is greater than or equal to the threshold value, a scene change is likely not to occur. Therefore, the processes at steps S 880  and S 881  are skipped and the index image is not generated. 
     Subsequently, at step S 882 , the control unit  2355  determines whether the user instructs to stop recording. If the user has not instructed to stop recording, the process returns to step S 871  and the processes subsequent to S 871  are repeated. If the user has instructed to stop recording, the process is completed. 
     Thus, a scene change is automatically detected during a recording operation and the index image is automatically generated. 
     The image output process to output an image to the image display device  2365  of the accumulating apparatus  2341  is described next with reference to a flow chart shown in  FIG. 75 . This process is executed when a user instructs to play back the recording image and output it. 
     At step S 901 , in response to the operation of the instruction input unit  2360  by the user, the total control unit  2359  causes an image recorded on the recording medium  2352  to be played back and to be output. The selection unit  2351  delivers an image played back from the recording medium  2352  to the display image generation unit  2358  via the selection unit  2357 . The display image generation unit  2358  converts the received image into a format that the image display device  2365  can display and outputs the converted image to the image display device  2365 , which displays the image. 
     At step S 902 , in response to the operation of the instruction input unit  2360  by the user, the total control unit  2359  determines whether the user has instructed to display the index image. If the user has not instructed to display the index image, the process returns to step S 901  and the processes subsequent to step S 901  are repeatedly executed. That is, the process to play back and output (display) the image recorded on the recording medium  2352  on the image display device  2365  continues. 
     In contrast, if the user has instructed to display the index image, the total control unit  2359 , at step S 903 , controls the index table  2356  to output the index image recorded in the index table  2356 . That is, the index table  2356  reads out a list of the index images and outputs the list to the display image generation unit  2358  via the selection unit  2357 . The display image generation unit  2358  outputs the list of the index images to the image display device  2365 , which displays the list. Thus, the list in which 3×3 index images are arranged is displayed on a screen. 
     By operating the instruction input unit  2360 , the user can select one of the plurality of displayed index images (the list of the index images). Thereafter, at step S 906 , the total control unit  2359  determines whether one of the index images displayed on the image display device  2365  is selected. If it is determined that no index image is selected, the process returns to step S 903  and the processes subsequent to step S 903  are repeatedly executed. That is, the list of the index images is continuously displayed by the image display device  2365 . 
     In contrast, if it is determined that one of the index image is selected (the user selects the desired index image from among the index images in the list), the total control unit  2359 , at step S 905 , plays back the recorded image starting from an image corresponding to the selected index image from the recording medium  2352 . The recorded image is output to the image display device  2365  via the selection unit  2351 , the selection unit  2357 , and the display image generation unit  2358 . The image display device  2365  displays the image. That is, if it is determined that one of the index image is selected, the total control unit  2359  reads out the additional information (time code, address, etc.) corresponding to the index image selected at step S 904  from the index table  2356 . The total control unit  2359  then controls the recording medium  2352  to play back the images starting from the image corresponding to the index image and output the images to the image display device  2365 , which displays the images. 
     At step S 906 , the total control unit  2359  determines whether the user has instructed to stop outputting the images. It is determined whether the user has instructed to stop outputting (displaying) the images by checking the operation of the instruction input unit  2360  by the user. If it is determined that the user has not input the stop instruction, the process returns to step S 901  and the processes subsequent to step S 901  are repeatedly executed. However, if it is determined that the user has input the stop instruction, the process is completed. 
     In addition, the accumulating apparatus  2341  can be applied even when the recording medium is, for example, a DVD or a video tape. 
     The above-described series of processes can be executed not only by hardware but also by software. When the above-described series of processes are executed by software, the programs of the software are downloaded from a network or a recording medium into a computer incorporated in dedicated hardware or a computer that can execute a variety of function by installing a variety of programs therein (e.g., a general-purpose personal computer). 
     In the present specification, the steps that describe the above-described series of processes include not only processes executed in the above-described sequence, but also processes that may be executed in parallel or independently. 
       FIG. 76  illustrates an example in which the present invention is applied to a security camera system. In a security camera system  2800 , an image captured by an image capturing unit  2801  including a CCD video camera is displayed on an image display  2802 . A tracking object detection unit  2803  detects an object to be tracked from an image input from the image capturing unit  2801  and outputs the detection result to an object tracking unit  2805 . The object tracking unit  2805  operates so as to track the object to be tracked specified by the tracking object detection unit  2803  in the image delivered from the image capturing unit  2801 . The object tracking unit  2805  basically has a configuration that is the same as that of the above-described object tracking apparatus  1  shown in  FIG. 1 . A camera driving unit  2804  drives the image capturing unit  2801  to capture an image at the center of which is a tracking point of the object to be tracked under the control of the object tracking unit  2805 . 
     A control unit  2806  includes, for example, a microcomputer and controls each component. A removable medium  2807  including a semiconductor memory, a magnetic disk, an optical disk, or a magnetooptical disk is connected to the control unit  2806  as needed. The removable medium  1711  provides a program and various types of data to the control unit  2806  as needed. 
     The operation of the monitoring process is described next with reference to a flow chart shown in  FIG. 77 . When the security camera system  2800  is powered on, the image capturing unit  2801  captures the image of a security area and outputs the captured image to the tracking object detection unit  2803 , the object tracking unit  2805 , and the image display  2802 . At step S 931 , the tracking object detection unit  2803  executes a process to detect the object to be tracked from the image input from the image capturing unit  2801 . For example, when a moving object is detected, the tracking object detection unit  2803  detects the moving object as the object to be tracked. The tracking object detection unit  2803  detects, for example, a point having the highest brightness or the center point of the object to be tracked as the tracking point and delivers information about the determined tracking point to the object tracking unit  2805 . 
     At step S 932 , the object tracking unit  2805  executes a tracking process to track the tracking point detected at step S 931 . This tracking process is the same as that of the above-described object tracking apparatus  1  shown in  FIG. 1 . 
     At step S 933 , the object tracking unit  2805  detects the position of the tracking point on the screen. At step S 934 , the object tracking unit  2805  detects a difference between the position of the tracking point detected at step S 933  and the center of the image. At step S 935 , the object tracking unit  2805  generates a camera driving signal corresponding to the difference detected at step S 934  and outputs the camera driving signal to the camera driving unit  2804 . At step S 936 , the camera driving unit  2804  drives the image capturing unit  2801  on the basis of the camera driving signal. Thus, the image capturing unit  2801  pans or tilts so that the tracking point is located at the center of the image. 
     At step S 937 , the control unit  2806  determines whether to terminate the monitoring process on the basis of the user instruction. If the user has not instructed to stop the monitoring process, the process returns to step S 931  and the processes subsequent to step S 931  are repeatedly executed. If the user has instructed to stop the monitoring process, it is determined at step S 937  that the process is completed. Thus, the control unit  2806  terminates the monitoring process. 
     As noted above, in the security camera system  2800 , a moving object is automatically detected as the tracking point and the image at the center of which is the tracking point is displayed on the image display  2802 . Thus, the monitoring process can be more simply and more reliably executed. 
       FIG. 78  illustrates another example of the configuration of the security camera system according the present invention. A security camera system  2900  includes an image capturing unit  2901 , an image display  2902 , an object tracking unit  2903 , a camera driving unit  2904 , a control unit  2905 , an instruction input unit  2906 , and a removable medium  2907 . 
     Like the image capturing unit  2801 , the image capturing unit  2901  includes, for example, a CCD video camera. The image capturing unit  2901  outputs a captured image to the image display  2902  and the object tracking unit  2903 . The image display  2902  displays the input image. The object tracking unit  2903  basically has a configuration that is the same as that of the above-described object tracking apparatus  1  shown in  FIG. 1 . The camera driving unit  2904  drives the image capturing unit  2901  to pan or tilt in a predetermined direction under the control of the object tracking unit  2903 . 
     The control unit  2905  includes, for example, a microcomputer and controls each component. The instruction input unit  2906  includes a variety of buttons and switches, and a remote controller. The instruction input unit  2906  outputs a signal corresponding to the user instruction to the control unit  2905 . A removable medium  2907  including a semiconductor memory, a magnetic disk, an optical disk, or a magnetooptical disk is connected to the control unit  2905  as needed. The removable medium  2907  provides a program and various types of data to the control unit  2905  as needed. 
     The operation of the control unit  2905  is described next with reference to a flow chart shown in  FIG. 79 . 
     At step S 961 , the control unit  2905  determines whether a tracking point is specified by a user. If the tracking point is not specified, the process proceeds to step S 969 , where the control unit  2905  determines whether the user has instructed to stop the processing. If the user has not instructed to stop the processing, the process returns to step S 961  and the processes subsequent to step S 961  are repeatedly executed. 
     That is, during this process, an image of the image capturing area captured by the image capturing unit  2901  is output to the image display  2902 , which displays the image. If the user (observer) stops the process to monitor the security area, the user operates the instruction input unit  2906  to instruct the control unit  2905  to stop the process. When the control unit  2905  is instructed to stop the process, the control unit  2905  stops the monitoring process. 
     On the other hand, if the user watches the image displayed on the image display  2902  and finds any potential prowler, the user specifies a point at which that potential prowler is displayed as the tracking point. A user specifies this point by operating the instruction input unit  2906 . When user specifies the tracking point, it is determined at step S 961  that the tracking point is specified and the process proceeds to step S 962 , where the tracking process is executed. The processes executed at steps S 962  through S 967  are the same as the processes executed at steps S 932  through S 937  shown in  FIG. 77 . That is, by performing this operation, the image capturing unit  2901  is driven so that the specified tracking point is located at the center of the screen. 
     At step S 967 , the control unit  2905  determines whether it is instructed to stop monitoring. If the control unit  2905  is instructed to stop monitoring, the control unit  2905  stops the process. However, if the control unit  2905  is not instructed to stop monitoring, the process proceeds to step S 968 , where the control unit  2905  determines whether it is instructed to stop tracking. For example, when the user identifies that the potential prowler who is specified as the tracking point is not a prowler, the user can operate the instruction input unit  2906  to instruct the control unit  2905  to stop tracking. If, at step S 968 , the control unit  2905  determines that it has not instructed to stop the tracking, the process returns to step S 962  and the processes subsequent to step S 962  are executed. That is, in this case, the operation to track the tracking point continues. 
     If, at step S 968 , the control unit  2905  determines that it has been instructed to stop the tracking, the tracking operation is stopped. The process returns to step S 961  and the processes subsequent to step S 961  are repeatedly executed. 
     Thus, in the security camera system  2900 , the image of the tracking point specified by the user is displayed at the center of the image display  2902 . Accordingly, the user can select any desired image and can carefully monitor the image. 
     The present invention can be applied to not only a television receiver and a security camera system but also a variety types of image processing apparatuses. 
     While the foregoing description is made with reference to image processing on a frame basis, the present invention is applicable to image processing on a field basis. 
     The above-described series of processes can be executed not only by hardware but also by software. When the above-described series of processes are executed by software, the programs of the software are downloaded from a network or a recording medium into a computer incorporated in dedicated hardware or a computer that can execute a variety of function by installing a variety of programs therein (e.g., a general-purpose personal computer). 
     As shown in  FIG. 76  or  78 , examples of this recording medium include not only the removable medium  2807  or  2907  distributed to users separately from the apparatus in order to provide users with a program, such as a magnetic disk (including a floppy disk), an optical disk (including a compact disk-read only memory (CD-ROM) and a digital versatile disk (DVD)), a magnetooptical disk (including a mini-disc (MD)), and a semiconductor memory, but also a ROM and a hard disk storing the program and incorporated in the apparatus that is provided to the users. 
     In the present specification, the steps that describe the program stored in the recording media include not only processes executed in the above-described sequence, but also processes that may be executed in parallel or independently. 
     In addition, as used in the present specification, “system” refers to a logical combination of a plurality of devices; the plurality of devices is not necessarily included in one body. 
       FIG. 80  illustrates an exemplary configuration of a security camera system according to the present invention. In a security camera system  3001 , an image captured by an image capturing unit  3021  including, for example, a CCD video camera is displayed on an image display  3023 . A tracking object detection unit  3024  detects an object to be tracked from the image input from the image capturing unit and output the detection result to an object tracking unit  3026 . The object tracking unit  3026  basically has the same structure as that of the above-described object tracking apparatus  1  shown in  FIG. 1 . 
     The object tracking unit  3026  operates so as to track a tracking point specified by the tracking object detection unit  3024  in the image delivered from the image capturing unit  3021 . An area setting unit  3025  sets a predetermined area around the object including the tracking point in the image captured by the image capturing unit  3021  and outputs positional information representing the position of the area to the image correction unit  3022 . The image correction unit  3022  corrects an image in the area set by the area setting unit  3025  in the image captured by the image capturing unit  3021  so as to remove blurring (blurring out of focus) from the image in the area and outputs that image to the image display  3023 . A camera driving unit  3029  drives the image capturing unit  3021  to capture an image at the center of which is the tracking point under the control of the object tracking unit  3026 . 
     A control unit  3027  includes, for example, a microcomputer and controls each component. A removable medium  3028  including a semiconductor memory, a magnetic disk, an optical disk, or a magnetooptical disk is connected to the control unit  3027  as needed. The removable medium  3028  provides a program and various types of data to the control unit  3027  as needed. The control unit  3027  also receives the user instruction (e.g., a command) via an input/output interface (not shown). 
     A monitoring process is described next with reference to a flow chart shown  FIG. 81 . When the security camera system  3001  is powered on, the image capturing unit  3021  captures an image of the security area and outputs the captured image to the image display  3023  via the tracking object detection unit  3024 , the object tracking unit  3026 , and the image correction unit  3022 . At step S 1001 , the tracking object detection unit  3024  executes a process to detect an object to be tracked from the image input from the image capturing unit  3021 . For example, when a moving object is detected, the tracking object detection unit  3024  detects, for example, a point having the highest brightness or the center point of the object to be tracked as the tracking point and outputs information about the determined tracking point to the object tracking unit  3026 . 
     At step S 1002 , the object tracking unit  3026  executes a tracking process to track the tracking point detected at step S 1001 . Thus, the tracking point (e.g., the eye or a center of a head) of the object (e.g., human being or animal) to be tracked in the image captured by the image capturing unit  3021  is tracked. The positional information indicating the tracking point is output to the area setting unit  3025 . 
     At step S 1003 , the area setting unit  3025  sets a predetermined area around the object to be tracked (e.g., a rectangle having a predetermined size at the center of which is the tracking point) to a correction area on the basis of the output from the object tracking unit  3026 . 
     At step S 1004 , the image correction unit  3022  executes an image correction process to correct the image inside the correction area set by the area setting unit  3025  in the image captured by the image capturing unit  3021 . The image correction process is described in detail below with reference to  FIG. 93 . This process results in the creation of a clear image without blurring of the image in the correction area. 
     At step S 1005 , the image display  3023  outputs the image corrected at step S 1004 , namely, the image captured by the image capturing unit  3021  in which only the image in the correction area is particularly clear. 
     At step S 1006 , the object tracking unit  3026  detects the movement of the object on the basis of the tracking result from the process at step S 1002  and generates a camera driving signal to drive the camera so that the image of the moving object can be captured. The object tracking unit  3026  then output the camera driving signal to the control unit  3029 . At step S 1007 , the camera driving unit  3027  drives the image capturing unit  3021  on the basis of the camera driving signal. Thus, the image capturing unit  3021  pans or tilts so that the tracking point is always located inside the screen. 
     At step S 1008 , the control unit  3027  determines whether to terminate the monitoring process on the basis of the user instruction. If the user has not instructed to stop the monitoring process, the process returns to step S 1001  and the processes subsequent to step S 1001  are repeatedly executed. If the user has instructed to stop the monitoring process, it is determined at step S 1008  that the process is completed. Thus, the control unit  3027  terminates the monitoring process. 
     Additionally, the control signal is output to the camera driving unit  3029  to drive the camera (the image capturing unit  3021 ) so that the camera tracks the detected object to be tracked on the basis of the information about the tracking point output from the tracking object detection unit  3024  and the tracking point is displayed inside the screen of the image display  3023  (the tracking point does not move outside the screen). Furthermore, the tracking result, such as the positional information about the tracking point on the screen, is output to the area setting unit  3025  and the control unit  3027 . 
       FIGS. 82A-C  illustrate examples of time-series images displayed on the image display  3023  in such a case.  FIG. 82A  illustrates an image of an object  3051  to be tracked captured by the image capturing unit  3021 . In these examples, the image of a human running to the left is captured as the object  3051 . In  FIG. 82B , the object  3051  moves from the position shown in  FIG. 82A  to the left. In  FIG. 82C , the object  3051  further moves from the position shown in  FIG. 82B  to the left. 
     The tracking object detection unit  3024  detects the object  3051  at step S 1001  shown in  FIG. 81  and outputs the eye of the object  3051  (human) to the object tracking unit  3026  as a tracking point  3051 A. At step S 1002 , the object tracking unit  3026  executes a tracking process. At step S 1003 , the area setting unit  3025  sets a predetermined area around the object  3051  to be tracked (the tracking point  3051 A) to a correction area  3052 . 
     As noted above, the object tracking unit  3026  tracks the object  3051  on the basis of the tracking point  3051 A. Accordingly, when the object  3051  moves, the tracking point  3051 A also moves and the tracking result (the position) is output to the area setting unit  3025 . Thus, as shown in  FIGS. 82A to 82C , as the object  3051  moves to the left, the correction area  3052  also moves to the left. 
     The correction area  3052  corresponding to the moving object  3051  (the tracking point  3051 A) is set as follows, for example.  FIG. 83  illustrates an example in which a rectangular area having a predetermined size is set around the tracking point as a correction area. In  FIG. 83 , a correction area  3071 A is set first. For example, a predetermined area at the center of which is the tracking point  3051 A is set as the first correction area  3071 A. If a user specifies the correction area, this area is set as the first correction area  3071 A. At that time, the area setting unit  3025  stores the coordinates (X, Y) of the upper left corner of the correction area  3071 A in the internal memory thereof. If the tracking point  3051 A of the object  3051  moves, the object tracking unit  3026  starts tracking so that information about the positions (or the moving distance) of the tracking point  3051 A in the X-axis direction (horizontal direction in the drawing) and in the Y-axis direction (vertical direction in the drawing) is delivered to the area setting unit  3025  as the tracking result. 
     Subsequently, the correction area is set on the basis of the above-described coordinates of the upper left corner. For example, when the tracking point  3051 A moves by x in the X-axis direction and by y in the Y-axis direction on the screen, the area setting unit  3025  adds x and y to the coordinates (X, Y) of the upper left corner of the correction area  3071 A to compute the coordinates (X+x, Y+y). The area setting unit  3025  stores these coordinates as the coordinates of the upper left corner of a new correction area  3071 B and sets the correction area  3071 B. If the tracking point  3051 A further moves by a in the X-axis direction and by b in the Y-axis direction, the area setting unit  3025  adds a and b to the coordinates (X+x, Y+y) of the upper left corner of the correction area  3071 A so as to compute the coordinates (X+x+a, Y+y+b). The area setting unit  3025  stores these coordinates as the coordinates of the upper left corner of a new correction area  3071 C and sets the correction area  3071 C. 
     Thus, as the object (the tracking point) moves, the correction area moves. 
     Additionally, as noted above, an image inside the correction area  3052  is subjected to the image correction process (at step S 1004  shown in  FIG. 81 ) performed by the image correction unit  3022  so that blurring of the image is removed. The image is then displayed on the image display  3023 . Accordingly, partial images of the images shown in  FIGS. 82A-C  inside the correction area  3052  are clearly displayed. In contrast, the image of the background  3053  outside the correction area  3052  is not clearly displayed compared with the image inside the area  3052 . 
     Thus, the object  3051  in the correction area  3052  of the image displayed on the image display  3023  is clearly displayed at all times. Therefore, a user watching the image display  3023  automatically views the object  3051 . As a result, for example, the user can find a prowler or a moving object more rapidly. In addition, since the object  3051  is clearly displayed, the user can correctly identify what (who) the moving object (e.g., human being) is. 
     As noted above, since the object tracking unit  3026  basically has the same structure as that of the above-described object tracking apparatus  1  shown in  FIG. 1 , the description is not repeated. 
     By configuring the object tracking unit  3026  shown in  FIG. 80  in the above-described manner, even when the object  3051  (see  FIG. 82 ) to be tracked rotates or even when the occlusion occurs, or even when the tracking point  3051 A of the object  3051  is not temporarily displayed due to a scene change, the object  3051  (the tracking point  3051 A) moving in the image can be accurately tracked. 
     Thus, the positional information about the tracking point  3051 A of the object  3051  to be tracked is output the area setting unit  3025  as the tracking result of the object tracking unit  3026  shown in  FIG. 80 . Accordingly, the area setting unit  3025  can set the above-described correction area  3052 . Thereafter, the image correction unit  3022  removes blurring (blurring out of focus) of the image in the area  3052 . 
     The configuration and the operation of the image correction unit  3022  shown in  FIG. 80  are described in detail next.  FIG. 84  is a block diagram of the detailed configuration of the image correction unit  3022 . In this example, the image correction unit  3022  includes a control signal generation unit  3741  for generating a control signal on the basis of the output signal of the area setting unit  3025  and delivering this control signal to each component, an image feature detection unit  3742  for detecting the feature of an input image, an address computing unit  3743  for computing an address on the basis of the control signal, a coefficient ROM  3744  for outputting a prestored predetermined coefficient on the basis of the address computed by the address computing unit  3743 , and a region extraction unit  3745  for extracting a plurality of pixels corresponding to a predetermined region in the input image. 
     The image correction unit  3022  further includes an inner-product computing unit  3746  and an image combining unit  3747 . The inner-product computing unit  3746  computes the inner product of the level of a pixel output from the region extraction unit  3745  and a coefficient output from the coefficient ROM  3744  and outputting the modified pixel level. The image combining unit  3747  combines the image in the correction area  3052  with the background  3053  and outputs the combined image. 
       FIG. 85  is a diagram illustrating control signals generated by the control signal generation unit  3741 . A control signal A is a signal used for identifying an area (the correction area  3052 ) to be modified in the input image. The control signal A is generated on the basis of the output from the area setting unit  3025  and is delivered to the region extraction unit  3745  and the image combining unit  3747 . A control signal B is a signal used for identifying a parameter a representing the level of blurring, which is described below. The control signal B is delivered to the address computing unit  3743 . The value of the parameter a may be determined by, for example, the user instruction via the control unit  3027  or may be determined in advance. 
     A control signal C is a signal used for instructing to switch a weight Wa of a relational expression used for solving a model expression of blurring, which is described below. The control signal C is delivered to the address computing unit  3743 . A control signal D is a signal used for instructing to switch a threshold value used for detecting the feature of an image. The control signal D is delivered to the image feature detection unit  3742 . The control signals C and D may be predetermined in consideration of the characteristic of the security camera system  3001 . Alternatively, the control signals C and D may be generated on the basis of the user instruction via the control unit  3027 . 
     The principal of blurring of an image is described next. Suppose that the focus of a camera is properly set and let a level X of a pixel of an image without blurring be a real value. Let a level Y of a pixel of an image with blurring out of focus be an observed value. When the coordinate of the image in the horizontal direction is represented by x and the coordinate of the image in the vertical direction is represented by y to identify a plurality of pixels of the image, the real value can is expressed as X(x, y) and the observed value can be expressed as Y(x, y). 
     According to the present invention, the following equation (6) is used as the model expression of blurring. In equation (6), the Gaussian function expressed by the following equation (7) is used. By convoluting the real value X(x, y) with the Gaussian function, the observed value Y(x, y) can be obtained. 
     
       
         
           
             
               
                 
                   
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     In equation (6), the parameter σ denotes the level of blurring. 
     According to equation (6), one observed value Y(x, y) can be obtained by weighting a plurality of real values X(x+i, y+j) that varies in accordance with variables i and j (−r&lt;i&lt;r, and −r&lt;j&lt;r) with a coefficient W. Accordingly, the level of one pixel of an image without blurring can be obtained on the basis of the levels of a plurality of pixels of an image with blurring. 
     In addition, the level of blurring varies depending on the above-described parameter σ. When the value of the parameter σ is relatively small, information about the real value does not widely spread with respect to the observed value. Thus, an image with less blurring is obtained. In contrast, when the value of the parameter σ is relatively large, information about the real value widely spreads with respect to the observed value. Thus, an image with relatively strong blurring is obtained. 
     As noted above, the level of blurring varies depending on the above-described parameter σ. Therefore, to accurately correct the blurring of an image, the value of the parameter σ needs to be appropriately determined. According to the present invention, the user specifies the value of the parameter σ. Alternatively, an optimum value may be preset in consideration of the characteristic of the security camera system  3001 . 
     The principal of blurring of an image is described in more detail next with reference to  FIGS. 86 to 89 .  FIG. 86A  is a diagram illustrating real values X 0  to X 8  of a given image when, for simplicity, pixels are horizontally arranged in one dimension.  FIG. 86C  is a diagram illustrating the observed values corresponding to  FIG. 86A .  FIG. 86B  is a diagram illustrating the magnitude of a coefficient W(i) in the form of a bar graph. In this example, the range of the variable i is −2&lt;i&lt;2. The middle bar represents a coefficient W(0). The bars represent W(−2), W(−1), W(0), W(1), and W(2) from the leftmost to the rightmost. 
     Here, the observed value Y 2  in  FIG. 86C  can be obtained according to equation (6) as follows:
 
 Y 2= W (−2) X 2+ W (−1) X 3+ W (0) X 4+ W (1) X 5+ W (2) X 6
 
     Similarly, to obtain the observed value Y 0  in  FIG. 86C , by performing the computation about the real values in a frame  3790 - 1  shown in  FIG. 87 , the observed value Y 0  can be obtained as follows:
 
 Y 0= W (−2) X 0+ W (−1) X 1+ W (0) X 2+ W (1) X 3+ W (2) X 4
 
     Furthermore, to obtain the observed value Y 1 , by performing the computation about the real values in a frame  3790 - 2  shown in  FIG. 87 , the observed value Y 1  can be obtained as follows:
 
 Y 1= W (−2) X 1+ W (−1) X 2+ W (0) X 3+ W (1) X 4+ W (2) X 5
 
     Still furthermore, the observed values Y 3  and Y 4  can be obtained in the same manner. 
       FIGS. 88 and 89  illustrate a relationship between  FIG. 86A  and  FIG. 86C  in two dimensions. That is, the level of each pixel in  FIG. 88  is an observed value and is obtained using the level of each pixel in  FIG. 89  as a real value. In this case, the observed value Y(x, y) corresponding to a pixel A shown in  FIG. 88  can be obtained as follows:
 
 Y ( x,y )= W (−2,−2) X ( x− 2 ,y− 2)+ W (−1,−2) X ( x− 1 ,y− 2)+ W (0,2) X ( x,y− 2) . . . + W (2,2) X ( x+ 2, y+ 2)
 
     That is, the observed value corresponding to the pixel A shown in  FIG. 88  can be obtained on the basis of the real values corresponding to 25 (=5×5) pixels indicated by a frame a at the center of which is a pixel A′ (corresponding to the pixel A) shown in  FIG. 89 . Similarly, the observed value corresponding to a pixel B (pixel on the right of the pixel A) shown in  FIG. 88  can be obtained on the basis of the real values corresponding to 25 pixels at the center of which is a pixel B′ (corresponding to the pixel B) shown in  FIG. 89 . The observed value corresponding to a pixel C shown in  FIG. 88  can be obtained on the basis of the real values corresponding to 25 pixels at the center of which is a pixel C′ (corresponding to the pixel C) shown in  FIG. 89 . The observed values Y(x+1, y) and Y(x+2, y) respectively corresponding to the pixels B and C shown in  FIG. 88  can be obtained by the following equations:
 
 Y ( x+ 1, y )= W (−2,−2) X ( x− 1, y− 2)+ W (−1,−2) X ( x,y− 2)+ W (0,−2) X ( x− 1, y− 2) . . . + W (2,2) X ( x+ 3, y+ 2)
 
 Y ( x+ 2, y )= W (−2,−2) X ( x,y− 2)+ W (−1,−2) X ( x+ 1, y− 2)+ W (0,−2) X ( x+ 2, y− 2) . . . + W (2,2) X ( x+ 4, y+ 2)
 
     After the observed values corresponding to all the pixels shown in  FIG. 88  are computed, the determinants of matrix expressed by the following equations (8) to (11) can be obtained: 
     
       
         
           
             
               
                 
                   
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     Here, if the inverse matrix of the matrix W f  in equation (11) can be solved, the real value X f  can be obtained on the basis of the observed value Y f . That is, pixels of an image without blurring can be obtained on the basis of pixels of an image with blurring, thus correcting the blurred image. 
     However, as described in relation to  FIGS. 86 to 89 , the determinants of matrix expressed by equations (8) to (11) include many pixels of a real value relative to pixels of an observed value. Therefore, it is difficult to obtain the inverse matrix (e.g., in the example shown in  FIG. 87 , five pixels of a real value are required for one pixel of an observed value). 
     Accordingly, in addition to equations (8) to (11), the relational expressions expressed by the following equations (12) to (15) are introduced:
 
 W   a ( p   1 ) W   1 ( p   2 )( X ( x,y )− X ( x,y− 1))=0  (12)
 
 W   a ( p   1 ) W   2 ( p   2 )( X ( x,y )− X ( x+ 1, y ))=0  (13)
 
 W   a ( p   1 ) W   3 ( p   2 )( X ( x,y )− X ( x,y+ 1))=0  (14)
 
 W   a ( p   1 ) W   4 ( p   2 )( X ( x,y )− X ( x− 1, y ))=0  (15)
 
     Equations (12) to (15) set limits to the difference between the levels of two adjacent pixels. When the real value to be obtained lies in a flat portion (a portion whose level has no significant difference from that of the adjacent pixel) of the image, there is no inconsistency. However, when the real value to be obtained lies in an edge portion (a portion whose level has a significant difference from that of the adjacent pixel) of the image, there is inconsistency. Thus, the corrected image may deteriorate. For this reason, to properly correct a blurred image, one of the four equations (12) to (15) needs to be appropriately used for each pixel so that the adjacent pixels do not cross the edge portion of the real values. 
     Therefore, the image feature detection unit  3742  determines the edge portion and the flat portion of the image to generate a code p 2  that indicates in which direction the image becomes flat (e.g., horizontal direction or vertical direction). The operation of the image feature detection unit  3742  is described in detail below with reference to  FIG. 94 . According to the present invention, it is assumed that the determination result of an edge portion and a flat portion in an input image (observed values) is equal to the determination result of an edge portion and a flat portion of the real values. 
     In equations (12) to (15), the functions W 1  to W 4 , which are functions of the code p 2 , are weighting functions. According to the present invention, by controlling these functions W 1  to W 4  in accordance with the code p 2 , one of the relational expressions can be selected and used for each pixel.  FIG. 90  illustrates the values of the functions W 1  to W 4  corresponding to the code p 2 . As the value of this weighting function increases, the portion becomes more flat. In contrast, as the value of this weighting function decreases, the portion becomes less flat (the possibility of being an edge increases). 
     The code p 2  consists of 4 bits. The bits indicate whether an image is flat in the upward direction, the right direction, the downward direction, and the left direction from the leftmost bit, respectively. If the image is flat in one of the directions, the corresponding bit is set to “1”. For example, the code p 2  of “0001” indicates that the image is flat from a pixel of interest in the left direction, but not flat in the other directions (i.e., an edge is present). Therefore, when the code p 2  is “0001”, the value of the weighting function W 4  increases and the weight of equation (15) has a large value compared with the weights of other equations (12) to (14). Thus, the code p 2  can change the weights of the four relational expressions. Accordingly, one of the four equations can be appropriately selected and used for each pixel so that the adjacent pixels do not cross the edge. 
     For example, as shown in  FIG. 91 , suppose that the image is flat from a pixel of interest in the upward direction and the left direction, and the image has edges in the right direction and the downward direction. By changing the weights of four equations (12) to (15), the limitations “Xa−Xb=0” and “Xa−Xc=0” are applied to the difference between the levels of adjacent pixels. However, the limitations “Xa−Xd=0” and “Xa−Xe=0” are not applied. It is noted that Xb, Xc, Xd, and Xe denote pixels adjacent to the pixel X of interest in the right direction, downward direction, upward direction, and left direction, respectively. 
     Additionally, in equations (12) to (15), a function Wa is a different weighting function. The value of the function Wa also varies in accordance with a code p 1 . By changing the value of the function Wa, the total noise and details of the corrected image can be controlled. When the value of the function Wa is large, the user feels little effect of noise in the corrected image, and therefore, the sense of noise decreases. In contrast, when the value of the function Wa is small, the user feels an enhanced effect of details in the corrected image, and therefore, the sense of details increases. It is noted that the code p 1  that changes the value of the function Wa corresponds to a control signal C shown in  FIG. 85 . 
     As noted above, the relational expressions expressed by equations (12) to (15) are introduced in addition to equations (8) to (11). Thus, the inverse matrix expressed as equation (16) can be solved. As a result, the real values can be obtained on the basis of the observed values.
 
 X   s   =W   s   −1   Y   s   (16)
 
     According to the present invention, a coefficient W s −1 to be multiplied by the observed value Y s  is prestored in the coefficient ROM  3744 . The determinant of matrix expressed by equation (16) (inner product) is computed by the inner-product computing unit  3746  with respect to the input image extracted by the region extraction unit  3745 . Thus, the computation of the inverse matrix is not necessary every time the image is corrected. The blurring can be corrected only by the inner-product computation. However, since the parameter σ and the above-described four relational expressions vary depending on an input image, the inverse matrix is computed for every possible combination of the parameter σ and the above-described four relational expressions. Thereafter, the addresses corresponding to the parameter σ and the code p 2  are determined. The different coefficients for those addresses are stored in the coefficient ROM  3744 . 
     However, if, for example, the combination of the weighting functions W 1  to W 4  is changed for each of 25 (=5×5) pixels in a frame (t) shown in  FIG. 89  and the four relational expressions are changed, the number of combinations is 15 (the number of combinations of the functions W 1  to W 4 ) powered by 25 (the number of pixels in the frame (t)). If the reverse matrix is computed for every combination, the number of coefficients becomes large. Since the capacity of the coefficient ROM  3744  is limited, the coefficient ROM  3744  could not store all the coefficients. In such a case, the code p 2  that is located at the center of the frame (t) is changed only for a pixel Xt so as to switch the relational expression. For pixels other than the pixel Xt in the frame (t), the code p 2  may be fixed to a pseudo value of “1111”, for example. Thus, the number of the combinations of the coefficient can be limited to 15. 
     In the foregoing description, to describe the principal of blurring (a model expression), the domain of the Gaussian function is determined to be −2≦(x, y)≦2. In practice, the domain of the Gaussian function is determined so as to support the parameter σ of a sufficiently large value. In addition, the relational expressions expressed as equations (12) to (15) are not limited thereto if the relational expressions can describe the feature of the image. Furthermore, in the case of the coefficient ROM  3744  having a limited capacity, the relational expressions are switched only for the center phase (Xt) of blurring. However, the present invention is not limited thereto. The method for switching the relational expressions may be changed depending on the capacity of the coefficient ROM  3744 . 
     A blur correction process performed by the image correction unit  3022  is described next with reference to  FIG. 92 . At step S 1801 , the image correction unit  3022  detects an area to be processed. The area to be processed is an area where blurring is corrected, namely, the correction area  3052 . This area is detected on the basis of a signal output from the area setting unit  3025 . 
     At step S 1802 , the image correction unit  3022  acquires the value of the parameter a. The value of the parameter σ may be specified by the user or may be determined in advance. At step S 1803 , the image correction unit  3022  also executes an image correction process, which is described below with reference to  FIG. 93 . By this process, the blurred image is corrected and is output. 
     Thus, blurring of the image in the correction area  3052  is removed, and therefore, a clear image can be obtained. 
     The image correction process at step S 1803  shown in  FIG. 92  is described in detail with reference to  FIG. 93 . 
     At step S 1821 , the image feature detection unit  3742  executes an image feature extracting process, which is described below with reference to  FIG. 94 . Thus, it is determined in which direction the image is flat with respect to the pixel of interest. The code p 2 , which is described with reference to  FIG. 90 , is generated and is output to the address computing unit  3743 . 
     At step S 1822 , the address computing unit  3743  computes the address of the coefficient ROM  3744 . For example, the address of the coefficient ROM  3744  consists of 4 bits corresponding to the code p 2  (the output of the image feature detection unit  3742 ), 4 bits indicating the value of the parameter σ (the control signal B shown in  FIG. 85 ), and 2 bits corresponding to the code p 1  used for switching the weighting functions Wa of the above-described four relational expressions (the control signal C shown in  FIG. 85 ). This address has 1024 (2 10 ) values ranging from 0 to 1023. The address computing unit  3743  computes the corresponding address on the basis of the output of the image feature detection unit  3742 , the control signal B, and the control signal C. 
     At step S 1823 , the address computing unit  3743  reads the coefficient from the coefficient ROM  3744  on the basis of the address computed at step S 1822  and delivers the readout coefficient to the inner-product computing unit  3746 . 
     At step S 1824 , the inner-product computing unit  3746  computes the inner product for each pixel on the basis of the coefficient read out at step S 1823  and outputs the result of the inner product computation to the image combining unit  3747 . Thus, as noted above, the real values can be obtained from the observed values, and therefore, the blurred image can be corrected. 
     At step S 1825 , the image combining unit  3747  executes an image combining process, which is described below with reference to  FIG. 97 . Thus, it is determined whether the processing result of the inner-product computing unit  3746  is output or the input image is directly output for each pixel. At step S 1826 , the image combining unit  3747  outputs the corrected and selected image. 
     The image feature detecting process at step S 1821  shown in  FIG. 93  is described next with reference to  FIG. 94 . At step S 1841 , the image feature detection unit  3742  extracts blocks. At step S 1842 , the image feature detection unit  3742  computes the difference between the blocks extracted at step S 1841  (the details are described below with reference to  FIG. 96 ). At step S 1843 , the image feature detection unit  3742  compares the block difference computed at step S 1842  with a predetermined threshold value. At step S 1844 , the image feature detection unit  3742  outputs the code p 2 , which represents the direction in which the image is flat with respect to the pixel of interest, on the basis of the comparison result. 
     The image feature detecting process is described in more detail with reference to  FIGS. 95 and 96 .  FIG. 95  is a block diagram of the detailed configuration of the image feature detection unit  3742 . On the left side of the drawing, block cutout units  3841 - 1  to  3841 - 5  are provided. For example, as shown in  FIGS. 96A to 96E , the block cutout units  3841 - 1  to  3841 - 5  extract 5 blocks, each including 9 (=3×3) pixels one of which is the pixel of interest indicated by a black circle (a pixel to be corrected at that time). 
     A block  3881  shown in  FIG. 96A  is a middle block at the center of which is the pixel of interest. The block  3881  is extracted by the block cutout unit  3841 - 5 . A block  3882  shown in  FIG. 96B  is a top block that is obtained by shifting the block  3881  upwards by one pixel. The block  3882  is extracted by the block cutout unit  3841 - 3 . A block  3883  shown in  FIG. 96C  is a left block that is obtained by shifting the block  3881  to the left by one pixel. The block  3883  is extracted by the block cutout unit  3841 - 4 . 
     A block  3884  shown in  FIG. 96D  is a bottom block that is obtained by shifting the block  3881  downwards by one pixel. The block  3884  is extracted by the block cutout unit  3841 - 1 . A block  3885  shown in  FIG. 96E  is a right block that is obtained by shifting the block  3881  to the right by one pixel. The block  3885  is extracted by the block cutout unit  3841 - 2 . At step S 1841 , the five blocks  3881  to  3885  are extracted for each pixel of interest. 
     Information about the pixels of each block extracted by the block cutout units  3841 - 1  to  3841 - 5  is output to block difference computing units  3842 - 1  to  3842 - 4 . For example, the block difference computing units  3842 - 1  to  3842 - 4  compute the difference between pixels in each block as follows. 
     Of the 9 pixels of the block  3881 , three pixels (levels of the pixels) in the uppermost row are denoted as a( 3881 ), b( 3881 ), and c( 3881 ) from the leftmost pixel. Three pixels in the middle row are denoted as d( 3881 ), e( 3881 ), and f( 3881 ) from the leftmost pixel. Three pixels in the lowermost row are denoted as g( 3881 ), h( 3881 ), and i( 3881 ) from the leftmost pixel. Similarly, of the 9 pixels of the block  3884 , three pixels (levels of the pixels) in the uppermost row are denoted as a( 3884 ), b( 3884 ), and c( 3884 ) from the leftmost pixel. Three pixels in the middle row are denoted as d( 3884 ), e( 3884 ), and f( 3884 ) from the leftmost pixel. Three pixels in the lowermost row are denoted as g( 3884 ), h( 3884 ), and i( 3884 ) from the leftmost pixel. The block difference computing unit  3842 - 1  computes a block difference B( 1 ) as follows:
 
 B (1)=| a (3881)− a (3884)|+| b (3881)− b (3884)|+| c (3881)− c (3884)|+ . . . +| i (3881)− i (3884)|
 
     That is, the block difference B( 1 ) is the sum of absolute differences between the levels of pixels in the block  3881  (middle) and the levels of the corresponding pixels in the block  3884  (bottom). Similarly, the block difference computing unit  3842 - 2  computes the sum of absolute differences between the levels of pixels in the block  3881  (middle) and the levels of the corresponding pixels in the block  3885  (right) so as to obtain a block difference B( 2 ). Furthermore, the block difference computing unit  3842 - 4  computes the sum of absolute differences between the levels of pixels in the block  3881  (middle) and the levels of the corresponding pixels in the block  3882  (top) so as to obtain a block difference B( 3 ). The block difference computing unit  3842 - 3  computes the sum of absolute differences between the levels of pixels in the block  3881  (middle) and the levels of the corresponding pixels in the block  3883  (left) so as to obtain a block difference B( 4 ). 
     At step S 1842 , as noted above, the block differences B( 1 ) to B( 4 ), which are the differences between the middle block and each of the blocks in the four horizontal and vertical directions, are computed. The results are output to the corresponding threshold value determination units  3843 - 1  to  3843 - 4  and a minimum direction determination unit  3844 . 
     The threshold value determination units  3843 - 1  to  3843 - 4  compare the block difference B( 1 ) to B( 4 ) with predetermined threshold values, respectively. It is noted that the threshold values are switched on the basis of the control signal D. If the block difference B( 1 ) to B( 4 ) are greater than the predetermined threshold values, respectively, the threshold value determination units  3843 - 1  to  3843 - 4  determine that the direction is an edge portion, and therefore, the threshold value determination units  3843 - 1  to  3843 - 4  output “0”. If the block difference B( 1 ) to B( 4 ) are less than the predetermined threshold values, respectively, the threshold value determination units  3843 - 1  to  3843 - 4  determine that the direction is an flat portion, and therefore, the threshold value determination units  3843 - 1  to  3843 - 4  output “1”. 
     At step S 1843 , the block difference is compared with the threshold value, as noted above. The output results of the threshold value determination units  3843 - 1  to  3843 - 4  are output to a selector  845  in the form of a 4-bit code. For example, if each of the block differences B( 1 ), B( 3 ), and B( 4 ) is less than the threshold value and the block difference B( 2 ) is greater than the threshold value, a code of “1011” is output. 
     In some cases, all of the block differences B( 1 ) to B( 4 ) are greater than the threshold values (i.e., the image has no flat portion). In such cases, a code of “0000” is output from the threshold value determination units  3843 - 1  to  3843 - 4 . However, as shown in  FIG. 90 , when the code p 2  is “0000”, the corresponding weighting functions W 1  to W 4  cannot be identified. Therefore, a selector  3845  determines whether the output result from the threshold value determination units  3843 - 1  to  3843 - 4  is “0000”. If the selector  3845  determines that the output result from the threshold value determination units  3843 - 1  to  3843 - 4  is “0000”, the selector  3845  outputs the output from the minimum direction determination unit  3844  as the code p 2 . 
     The minimum direction determination unit  3844  determines the minimum value among the block differences B( 1 ) to B( 4 ) and outputs a 4-bit code corresponding to the determination result to the selector  3845  at the same time as the threshold value determination units  3843 - 1  to  3843 - 4  output the code. For example, if it is determined that the block difference B( 1 ) is the minimum among the block differences B( 1 ) to B( 4 ), the minimum direction determination unit  3844  outputs a code of “1000” to the selector  3845 . 
     This design allows the code “1000” to be output from the minimum direction determination unit  3844  as the code p 2  even when the threshold value determination units  3843 - 1  to  3843 - 4  output the code “0000”. When the output result from the threshold value determination units  3843 - 1  to  3843 - 4  is not “0000”, the output result from the threshold value determination units  3843 - 1  to  3843 - 4  is output as the code p 2 . At step S 3844 , the code p 2  is thus generated and is output to the address computing unit  3743 . 
     The image combining process at step S 1825  shown in  FIG. 93  is described next with reference to  FIG. 97 . At step S 1861 , the image combining unit  3747  computes the degree of dispersion of pixels on the basis of the output result from the inner-product computing unit  3746 . Thus, the degree of dispersion of the pixels around the pixel of interest can be computed. At step S 1862 , the image combining unit  3747  determines whether the degree of dispersion computed at step S 1862  is greater than a predetermined threshold value. 
     If, at step S 1862 , it is determined that the degree of dispersion is greater than the threshold value, the image combining unit  3747 , at step S 1863 , sets an input-image switching flag to ON. In contrast, if it is determined that the degree of dispersion is not greater than the threshold value, the image combining unit  3747 , at step S 1864 , sets an input-image switching flag to OFF. 
     If the inner-product computing unit  3746  performs the inner product computation on a pixel in a partial area of the input image where blurring does not occur, the activity of the image around the pixel may increase, and therefore, the quality of the image may deteriorate. So, if the degree of dispersion is greater than the predetermined threshold value, it is determined that the pixel is a deteriorated pixel and the input-image switching flag is set to ON. The pixel whose input-image switching flag is set to ON is replaced with the pixel of the input image (i.e., the pixel is returned to the original pixel) when the pixel is output. 
     At step S 1865 , the image combining unit  3747  determines whether all the pixels are checked. If it is determined that all the pixels have not been checked, the process returns to step S 1861  and the processes subsequent to step S 1861  are repeatedly executed. If, at step S 1865 , it is determined that all the pixels have been checked, the image combining unit  3747 , at step S 1866 , combines the image having no blurring in the correction area  3052  with the image of the background  3053  and outputs the combined image to the image display  3023 . 
     Thus, it is determined whether the result of the inner product computation is to be output or the pixel of the input image is to be directly output for each pixel. This design can prevent an image from deteriorating by correcting a partial image without blurring in the input image. 
     This phenomenon is now herein discussed in more detail with reference to  FIGS. 98 and 99 .  FIG. 98  is a block diagram of an exemplary configuration of the image combining unit  3747 . The output result of the inner-product computing unit  3746  is input to a block cutout unit  3901 . As shown in  FIG. 99 , the block cutout unit  3901  cuts out 9 (=3×3) pixels a 1  to a 9  at the center of which is a pixel of interest a 5  and outputs these pixels to a dispersion computing unit  3802 . The dispersion computing unit  3802  computes the degree of dispersion as follows: 
     
       
         
           
             
               
                 
                   v 
                   = 
                   
                     
                       ∑ 
                       
                         
                           
                               
                           
                           * 
                         
                         = 
                         1 
                       
                       9 
                     
                     ⁢ 
                     
                       
                         ( 
                         
                           
                             a 
                             * 
                           
                           - 
                           m 
                         
                         ) 
                       
                       2 
                     
                   
                 
               
               
                 
                   ( 
                   17 
                   ) 
                 
               
             
           
         
       
     
     where m denotes the average of the 9 pixels (the pixel level) in a block, and v denotes the sum of square differences between each pixel and the average, namely, the degree of dispersion of the pixels in the block. At step S 1861 , the degree of dispersion is thus computed and the computation result is output to a threshold value determination unit  3903 . 
     The threshold value determination unit  3903  compares the output result (the degree of dispersion) from a dispersion computing unit  3902  with a predetermined threshold value. If it is determined that the degree of dispersion is greater than the threshold value, the image combining unit  3747  controls a selection unit  3904  to set the input-image switching flag corresponding to the pixel of interest to ON. If it is determined that the degree of dispersion is not greater than the threshold value, the image combining unit  3747  controls a selection unit  3904  to set the input-image switching flag corresponding to the pixel of interest to OFF. At steps S 1862  through S 1864 , it is thus determined whether the degree of dispersion is greater than the threshold value. The input-image switching flag is set on the basis of the determination result. 
     Subsequently, a switching unit  3905  switches between the final processing result of the selection unit  3904  and a pixel of the input image. The switching unit  3905  then outputs the selected one. That is, the pixels of the image in the correction area  3052  represent the final processing result of the selection unit  3904 , whereas the pixels of the image of the background  3053  represent the pixels of the input image. The image is thus switched. 
     Thus, the object  3051  ( FIG. 82 ) is tracked. Only the image in the correction area  3052  including the object  3051  is updated (corrected) so that blurring of the image is removed, and therefore, is clearly displayed. In contrast, since the image of the background  3053  is displayed without the blurring removed, the user can automatically and carefully watch the object  3051 . 
     In the foregoing description, the image correction unit  3022  corrects the image in the correction area  3052  of the image captured by the image capturing unit  3021  so that the blurring of the image is removed. However, the image correction unit  3022  may correct the image in the correction area  3052  without removing blurring of the image so that the brightness and color setting of each pixel in the area are changed and the image in the area is simply highlighted. According to this design, although the user could not accurately view the object  3051 , the user can automatically and carefully watch the object  3051 . Additionally, compared with the correction to remove blurring of the image, the configuration of the image correction unit  3022  can be simplified. As a result, the object tracking apparatus  1  can be achieved at a low cost. 
     The above-described series of processes can be realized not only by hardware but also by software. When the above-described series of processes are executed by software, the programs of the software are downloaded from a network or a recording medium into a computer incorporated in dedicated hardware or a computer that can execute a variety of function by installing a variety of programs therein (e.g., a general-purpose personal computer). 
     In the present specification, the steps that describe the program stored in the recording media include not only processes executed in the above-described sequence, but also processes that may be executed in parallel or independently. 
     REFERENCE NUMERALS 
       1  object tracking apparatus,  11  template matching unit,  12  motion estimation unit,  13  scene change detection unit,  14  background motion estimation unit,  15  region-estimation related processing unit,  16  transfer candidate storage unit,  17  tracking point determination unit,  18  template storage unit,  19  control unit