Abstract:
Provided is a transition area detection device capable of detecting, with high precision, a transition area in a space without using a positioning sensor. The transition area detection device has a corresponding point search-use feature point selection unit for selecting feature points used for determining a reference image from among feature points of an input image (captured image), a geometric transformation parameter calculation-use feature point selection unit for selecting feature points used for calculating geometric transformation parameters from among feature points of the input image and feature points of the reference image, and a degree of similarity calculation-use feature point selection unit; for selecting feature points used for obtaining a degree of similarity between the captured image and the reference image from among the feature points of the input image and the feature points of the reference image.

Description:
TECHNICAL FIELD 
       [0001]    The present invention relates to an apparatus and a method that detect a varied region in a space on the basis of a captured image acquired by a portable camera. 
       BACKGROUND ART 
       [0002]    Recently, systems that perform monitoring with wearable cameras or similar devices have been put to practical use. One of such systems detects items that are taken away or left behind on the basis of images from a wearable camera worn by a security guard. An item that is taken away (for example, stolen) is detected by identifying an item that appears in registered images but does not appear in the captured image. On the other hand, an item that is left behind (for example, planting a dangerous item, such as a bomb) is detected by identifying an item that does not appear in the registered images but appears in the captured image. 
         [0003]    An image captured by a wearable camera varies depending on the position, orientation, and other parameters of the camera. Thus, information on the captured space (positioning information) is required. That is, a wearable camera includes sensors, such as a GPS, a gyro-sensor, and a magnetic direction sensor, which acquire positioning information serving as attribute information of a captured image. Then, an image corresponding to the positioning information is selected from the registered images (hereinafter, this image is referred to as “reference image”). In other words, a reference image capturing the same space as that in the captured image is identified by the positioning information. A comparison of the reference image and the captured image allows a varied region to be detected in the space. Based on such detection, an item taken away or left behind is identified as described above. 
         [0004]    To detect a varied region in a space with an image captured by a portable camera and a reference image, the captured image and the reference image must first be aligned. In other words, a reference image that corresponds to the captured image must be selected. 
         [0005]    There are two possible approaches of alignment: 
         [0006]    An approach using sensors, such as a GPS, as mentioned above; and 
         [0007]    An approach involving image processing such as pattern matching of images. 
         [0008]    A technique of alignment by image processing is described in, for example, Patent Document 1. 
       CITATION LIST 
     Patent Literature 
       [0000]    
       
         PTL 1 
         Japanese Patent Application Laid-Open No. 2003-242509 
       
     
       Non-Patent Literature 
       [0000]    
       
         NPL 1 
         D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoiunts”, International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004 
         NPL 2 
         N. Katayama and S. Satoh, “The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries”, Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, pp. 369-380, 1997 
       
     
       SUMMARY OF INVENTION 
     Technical Problem 
       [0015]    The technique described in PTL I originally does not presume selection of a reference image capturing the same space as that in a captured image under the assumption that the captured image includes a region that has varied from the corresponding region in the reference image. That is, conventional image processing represented by PTL 1 does not take account of a partial difference between a captured image and a reference image. Thus, a reference image capturing the same space as that in a captured image might not appropriately be selected through pattern matching. With the technique described in PTL 1, pattern matching is performed on local characteristics. Thus, the pattern matching is likely to be affected by the varied region in the captured image, and as a result, an inappropriate reference image is more likely to be selected. That is, the result of pattern matching of local characteristics is affected by items that appear in the captured image but do not appear in the reference image or, otherwise, by items that appear in the reference image but do not appear in the captured image. This is not preferred for the selection of a reference image capturing the same space. 
         [0016]    If an appropriate reference image is not selected (i.e., if a reference image capturing the same space as that in the capture image is not selected), the accuracy of detection of a varied region decreases obviously. 
         [0017]    Positioning sensors, such as a GPS, have a disadvantage in that they are only operable in limited locations. GPSs can only be used outdoors. A possible way for positioning other than GPS is UWB, which requires a receiver installed inside the facility, causing the system to be complicated. In either case, alignment of a captured image and a reference image by sensors has a disadvantage in that it can only be performed in limited locations and/or requires a complicated structure in addition to a camera, compared with alignment performed by image processing. 
         [0018]    An object of the present invention is to provide a detecting apparatus and a method that accurately detect a varied region in a space, without positioning sensors. 
       Solution to Problem 
       [0019]    An aspect of the varied-region detecting apparatus according to the present invention includes: a characteristic-point detecting section that detects characteristic points in a captured image; a registered-image database that stores a plurality of registered images; a first characteristic-point selecting section that selects characteristic points to be used for determination of a reference image from the characteristic points in the captured image; a reference-image determining section that matches the captured image to every registered image based on the characteristic points selected by the first characteristic-point selecting section and characteristic points in the registered images stored in the registered-image database and determines a registered image having the highest matching with the captured image as a reference image among the registered images; a second characteristic-point selecting section that selects characteristic points to be used for calculation of a geometric transformation parameter from the characteristic points in the captured image and characteristic points in the reference image; a geometric-transformation-parameter calculating section that calculates the geometric transformation parameter based on the characteristic points selected by the second characteristic-point selecting section; a third characteristic-point selecting section that selects characteristic points to be used for determination of a similarity between the captured image and the reference image from the characteristic points in the captured image and the characteristic points in the reference image; a similarity calculating section that geometrically transforms the characteristic points selected by the third characteristic-point selecting section using the geometric transformation parameter calculated by the geometric-transformation-parameter calculating section and calculates the similarity between the characteristic points in the captured image and the characteristic points in the reference image, after the geometric transformation; and a varied-region identifying section that identifies a varied region based on the similarity determined by the similarity calculating section. 
         [0020]    An aspect of the method according to the present invention of detecting a varied region calculates a similarity between a captured image and a reference image and detects a varied region in the captured image based on the similarity, the method including: a first characteristic-point selection step of selecting characteristic points to be used for determination of the reference image from characteristic points in the captured image; a second characteristic-point selection step of selecting characteristic points to be used for calculation of a geometric transformation parameter from the characteristic points in the captured image and the characteristic points in the reference image; and a third characteristic-point selection step of selecting characteristic points to be used for determination of the similarity between the captured image and the reference image from the characteristic points in the captured image and the characteristic points in the reference image. 
       Advantageous Effects of Invention 
       [0021]    The present invention enables independent selection of characteristic points suitable for determining a reference image, characteristic points suitable for calculating a geometric transformation parameter, and characteristic points suitable for calculating similarity, thus enabling determination of an appropriate reference image, an appropriate geometric transformation parameter, and accurate similarity, without excess calculation. As a result, a varied region can be precisely determined with a reduced calculation load. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0022]      FIG. 1  is a block diagram of the configuration of a varied-region detecting apparatus according to an embodiment of the present invention; 
           [0023]      FIG. 2A  illustrates the tree structure of an SR-tree, and  FIG. 2B  illustrates the data structure of a leaf; 
           [0024]      FIG. 3  is flow chart of the procedures carried out by a corresponding-point searching section; 
           [0025]      FIG. 4  illustrates information stored in a registered-image database; 
           [0026]      FIG. 5  is a flow chart of the procedures carried out by a reference-image determining section; 
           [0027]      FIG. 6A  is a flow chart of the procedures carried out by a similarity calculating section; 
           [0028]      FIG. 6B  is a flow chart of the procedures carried out by the similarity calculating section; and 
           [0029]      FIG. 7  illustrates images of varied-region detection carried out by the varied-region detecting apparatus. 
       
    
    
     DESCRIPTION OF EMBODIMENTS 
       [0030]    Now, embodiments of the present invention will be described in detail with reference to the drawings. 
         [0031]      FIG. 1  illustrates the configuration of a varied-region detecting apparatus according to an embodiment of the present invention. Varied-region detecting apparatus  10  inputs captured image S 1  to characteristic-point detecting section  11 . Captured image S 1  is acquired by a portable camera, such as a wearable camera. 
         [0032]    Characteristic-point detecting section  11  detects characteristic points in captured image S 1 . A characteristic point may be detected as a point corresponding to an extremum of multiple Difference-of-Gaussian (DOG) images generated from the difference in different smoothened images, such as those used in scale-invariant feature transform (SIFT). Characteristic-point extraction by DOG is a known technique described in, for example, NPL 1, and thus, a description thereof is omitted. Characteristic-point detecting section  11  detects multiple characteristic points in one captured image. The detected characteristic-point information S 2  is sent to characteristic-value calculating section  12 . 
         [0033]    Characteristic-value calculating section  12  calculates and outputs characteristic values S 3  for the characteristic points detected by characteristic-point detecting section  11 . The calculated characteristic values are preferably characteristic values that have constant rotation and scale, such as those used in SIFT. In this embodiment, the characteristic values are gradient information (multidimensional vector information) in the vicinity of the characteristic points. 
         [0034]    Characteristic-point selecting section  13  for corresponding-point search selects characteristic points to be used by corresponding-point searching section  14  from the characteristic points detected by characteristic-point detecting section  11 . Specifically, characteristic-point selecting section for corresponding-point search selects only sparse characteristic points in the space of characteristic values calculated by characteristic-value calculating section  12  from the characteristic points detected by characteristic-point detecting section  11 . Sparse characteristic points have no other characteristic points in their neighbors. In other words, the sparse characteristic points selected by characteristic-point selecting section  13  for corresponding-point search are sparser than the characteristic points selected by characteristic-point selecting section  20  for similarity calculation, which will be described below. 
         [0035]    Selection of such sparse characteristic points allows selection of an appropriate reference image even when an item newly appears or disappears in an input image (captured image). That is, selection of sparse characteristic points reduces the effect of local variation in the input image. As a result, the possibility of selection of an inappropriate reference image decreases. 
         [0036]    Corresponding-point searching section  14  searches the registered images for characteristic points (corresponding points) at which a distance between characteristic values with respect to each of the Nf characteristic points in the input image is smaller than or equal to a threshold. The distance between characteristic values refer to Euclidean distance. In this embodiment, the registered images are not directly used, but instead, corresponding points are searched for on the basis of characteristic-value indices stored in a characteristic-value index section. In this way, the search of corresponding points can be more efficient than direct use of the registered images. 
         [0037]    Characteristic-value index section  15  stores the characteristic values of all characteristic points contained in the registered images stored in registered-image database  17 . Characteristic-value index section  15  has an index structure, such as an SR-tree, that enhances the search efficiency of corresponding points. The SR-tree is a known technique described in, for example, PTL 2, and thus, a description thereof is omitted. 
         [0038]      FIG. 2A  illustrates the structure of an SR-tree, and  FIG. 2B  illustrates the data structure of a leaf. As illustrated in  FIG. 2B , each entry of a leaf of the SR-tree stores, in addition to the characteristic values, the identification numbers (IDs) of the original registered images containing the characteristic values. 
         [0039]      FIG. 3  illustrates the procedures performed by corresponding-point searching section  14 . For each characteristic point in a captured image, corresponding-point searching section  14  searches the registered images for a plurality of corresponding points. For example, a p-th characteristic point in the captured image has Kp corresponding points in the registered images. In Step ST 11 , corresponding-point searching section  14  selects one characteristic point in the input image (reference image) and acquires the corresponding characteristic value. Step ST 12  acquires, through a nearest neighbor search, Kp characteristic points nearest to the characteristic value acquired in Step ST 11  as corresponding points in the registered images. Step ST 13  determines whether corresponding points for all characteristic points in the input image (captured image) have been searched for. If the result is negative in Step ST 13  (NO in Step ST), a search of the corresponding points for the next characteristic point is performed by repeating Steps ST 11  and ST 12  until the result is positive in Step ST 13  (YES in Step ST), and the search of corresponding points ends. 
         [0040]    In this embodiment, characteristic-value index section  15  is provided. Alternatively, the registered images may be directly searched for corresponding points. 
         [0041]    Reference-image determining section  16  casts one vote for each original registered image containing a corresponding point searched by corresponding-point searching section  14  on the basis of the corresponding-point information from corresponding-point searching section  14  and the registered-image information from registered-image database  17 . Reference-image determining section  16  repeats the voting process for every corresponding point searched for every characteristic point in the input image (reference image). Then, reference-image determining section  16  determines the registered image that has received the most votes as the reference image for the input image. 
         [0042]    Reference-image determining section  16  preferably casts a vote weighted in accordance with the distance between characteristic values calculated by corresponding-point searching section  14 . In this way, the results of the voting will contain the certainty of the corresponding points, and thus, a more appropriate reference image is selected as the reference image. 
         [0043]    As illustrated in  FIG. 4 , each record stored in registered-image database  17  contains a registered image ID, a characteristic point ID detected from the registered image, the coordinate of the characteristic point, and the characteristic value of the characteristic point. For each registered image, registered-image database  17  holds multiple records corresponding to the respective characteristic points detected in the registered image. 
         [0044]      FIG. 5  illustrates the procedures carried out by reference-image determining section  16 . In Step ST 21 , reference-image determining section  16  inputs every corresponding point acquired through the search of corresponding points to a search result list. The search result list contains the characteristic point IDs of the corresponding points and the distances between characteristic values of the characteristic points in the input image and the corresponding points. Step ST 22  acquires one of the corresponding points from the search result list. Step ST 23  acquires the image ID of the original image for the corresponding point from registered-image database  17 . 
         [0045]    Subsequent Step ST 24  determines whether the acquired image ID is contained in a reference-image candidate list. The reference-image candidate list contains image IDs of the registered images and the number of votes. In other words, the list contains the number of votes for each registered image. If the result is positive in Step ST 24  (YES in Step ST 24 ), the process goes to Step ST 25  to accumulate the number of votes for the corresponding image ID in the reference-image candidate list. In contrast, if the result is negative in Step ST 24  (NO in Step ST 24 ), the process goes to Step ST 26  to add the corresponding image ID to the reference-image candidate list. 
         [0046]    Step ST 27  determines whether processing of every characteristic point in the search result list has been carried out. If the result is positive in Step ST  27  (YES in Step ST 27 ), the process goes to Step ST 28  to set the registered image that has received the most votes in the reference-image candidate list as the reference image. In contrast, if the result is negative in Step ST 27  (NO in Step ST 27 ), the process returns to Step ST 22 . 
         [0047]    Characteristic-point selecting section  18  for geometric-transformation-parameter calculation selects reference characteristic points to be used by geometric-transformation-parameter calculating section  19 . Specifically, characteristic-point selecting section  18  for geometric-transformation-parameter calculation selects a certain number of characteristic points in the input image detected by characteristic-point detecting section  11  in an ascending order from the characteristic point having the smallest distance of characteristic value to a characteristic point in the reference image searched by corresponding-point searching section  14 . Characteristic points that are closer to the characteristic points already selected than a predetermined distance in the coordinate space are not selected. 
         [0048]    In other words, characteristic-point selecting section  18  for geometric-transformation-parameter calculation selects characteristic points in the input image that have similarities greater than or equal to a predetermined value in comparison with the characteristic points in the reference image. As a result, the accuracy of the correspondence between the input image and the reference image can be enhanced. Characteristic-point selecting section  18  for geometric-transformation-parameter calculation selects characteristic points at which a distance in the coordinate space between the characteristic points in the input image and the characteristic points in the reference image is greater than or equal to a predetermined value. This can enhance the accuracy of geometric transformation. 
         [0049]    Geometric-transformation-parameter calculating section  19  calculates geometric transformation parameters that represent geometric changes between the input image and the reference image. The geometric transformation described in this embodiment is affine transformation. Geometric-transformation-parameter calculating section  19  acquires multiple pairs of reference points, each pair consisting of a characteristic point in the input image and a corresponding characteristic point in the reference image, and calculates affine transformation parameters through the method of least squares. The pairs of reference points are the characteristic points selected by characteristic-point selecting section  18  for geometric-transformation-parameter calculation. The characteristic points in the input image that do not have corresponding characteristic points in the reference image are not included in the pairs of reference points. Affine transformation parameters can be determined through the method of least squares if at least three pairs of reference points are acquired. Inverse affine transformation is further carried out. Inverse affine transformation is geometric transformation from the reference image to the input image. 
         [0050]    The affine transformation is represented by the following expressions: 
         [0000]      [1] 
         [0000]    
       
      
       x′=ax+by+c  
      
     
         [0000]      and 
         [0000]        y′=dx+ey+f   (expression 1)
 
         [0000]    where (x,y) is the coordinates of a characteristic point in the input image (captured image), and (x′,y′) is the coordinates of a characteristic point in the reference image. 
         [0051]    Characteristic-point selecting section  20  for similarity calculation selects characteristic points in the input image and the reference image to be used by similarity calculating section  21 . Specifically, characteristic-point selecting section  20  for similarity calculation does not select such characteristic points in the input image that a distance between characteristic values of the characteristic points in the input image and the characteristic points in the reference image determined through the search of corresponding points is larger than a predetermined threshold. In other words, only characteristic points in the input image that have a distance smaller than or equal to the predetermined threshold between the characteristic values of the characteristic points in the input image and the characteristic points in the reference image determined through the search of corresponding points are selected as characteristic points to be used by similarity calculating section  21 . Characteristic-point selecting section  20  for similarity calculation does not select characteristic points that are closer to the characteristic points already selected than a predetermined distance in the coordinate space. In this way, characteristic points in regions in an image that clearly have not varied and characteristic points that are disposed unnecessarily close to each other in the coordinate space can be excluded, avoiding excess similarity calculation. 
         [0052]    Similarity calculating section  21  calculates the distance between the characteristic values of corresponding characteristic points in the input image and the reference image to determine the similarity. Specifically, similarity calculating section  21  first performs affine transformation of the characteristic points in the input image with the transformation parameters calculated by geometric-transformation-parameter calculating section  19 . Then, similarity calculating section  21  searches for characteristic points in the reference image that are disposed near the coordinates of the affine-transformed characteristic points in the input image and then, calculates the distances between characteristic values of the characteristic points. Inversely, similarity calculating section  21  searches for characteristic points in the input image that are disposed near the coordinates determined through inverse transformation of the characteristic points in the reference image and then, similarly, calculates the distances between characteristic values of the characteristic points. 
         [0053]    Similarity calculating section  21  prepares a corresponding-point list containing coordinates of the characteristic points in the input image and the distances of the characteristic values between the characteristic points in the input image and the corresponding characteristic points in the reference image. If there are no characteristic points in the input image corresponding to the characteristic points in the reference image, the coordinate points calculated through affine transformation of the reference image are input to the corresponding-point list as coordinates of the characteristic points in the input image. If there are no characteristic points in the input image or reference image corresponding to the characteristic points in the reference image or input image, the distances between the characteristic values are set as a sufficiently large value (i.e., have low similarity). 
         [0054]      FIGS. 6A and 6B  illustrate the procedures carried out by similarity calculating section  21 . In Step ST 31 , similarity calculating section  21  selects one characteristic point in the input image. Subsequent Step ST 32  determines whether the characteristic point is unequal to a reference point. This reference point refers to the reference point used in the parameter calculation by geometric-transformation-parameter calculating section  19 . If similarity calculating section  21  determines that the characteristic point is unequal to a reference point (YES in Step ST  32 ), the process goes to Step ST 33 . Alternatively, if similarity calculating section  21  determines that the characteristic point is equal to a reference point (NO in Step ST 32 ), the process goes to Step ST 40 . 
         [0055]    Step ST 33  calculates the coordinates of the affine-transformed characteristic point. Subsequent Step ST 34  determines whether the coordinate point acquired through affine transformation appears in the reference image. If similarity calculating section  21  determines that the coordinate point acquired through affine transformation appears in the reference image (YES in Step ST 34 ), the process goes to Step ST 35 . Alternatively, if similarity calculating section  21  determines that the coordinate point acquired through affine transformation does not appear in the reference image (NO in Step ST 34 ), the process goes to Step ST 40 . 
         [0056]    Step ST 35  searches for a characteristic point in the reference image closest to the coordinate point acquired through affine transformation. Subsequent Step ST 36  determines whether the distance in the coordinate space between the coordinate of the characteristic point in the reference image searched for in Step ST 35  and the coordinate point acquired through affine transformation is smaller than or equal to a threshold. The threshold takes account of the error in the affine transformation. That means, if the result is positive in Step ST 36  (YES in Step ST 36 ), a characteristic point in the registered image corresponding to the affine-transformed characteristic point in the input image appears, and then, the process goes to Step ST 37 . In contrast, if the result is negative in Step ST 36  (NO in Step ST 36 ), a characteristic point in the registered image corresponding to the affine-transformed characteristic point in the input image does not appear even in consideration of the error in the affine transformation, and then, the process goes to Step ST 39 . 
         [0057]    Step ST 37  calculates the distance between characteristic values of the affine-transformed characteristic point in the input image and the corresponding characteristic point in the reference image. Then, Step ST 38  adds the coordinates of the characteristic points and the distance between the characteristic values to the corresponding-point list. 
         [0058]    Step ST 39  adds the coordinates of the characteristic points and a sufficiently large distance between characteristic values to the corresponding-point list. The “sufficiently large distance between characteristic values” represents a value that can be identified as a varied region by Subsequent varied-region identifying section  22 . A large distance between characteristic values indicates low similarity. 
         [0059]    Subsequent Step ST 40  determines whether the processing for all characteristic points in the input image has been completed. If completed (YES in Step ST 40 ), the process goes to Step ST 41 . If not completed (NO in Step ST 40 ), the process returns to Step ST 31  to repeat the same process for the next characteristic point. 
         [0060]    The processes of Steps ST 31  to ST 40  detects an item (i.e., varied region) that does not appear in the registered image but appears in the captured image, such as an item that has been left behind. Specifically, Step ST 39  sets a distance between characteristic values in such a varied region, the distance being large enough to be identified as a varied region by subsequent varied-region identifying section  22 . 
         [0061]    In contrast, the processes of Steps ST 41  to ST 50 , which will be described below, detects an item (i.e., varied region) that appear in the registered image but does not appear in the captured image, such as an item that has been taken away. 
         [0062]    In Step ST 41 , similarity calculating section  21  selects one characteristic point in the reference image. Subsequent Step ST 42  determines whether the characteristic point selected in Step ST 41  is absent in the corresponding-point list. If similarity calculating section  21  determines that the characteristic point is absent in the corresponding-point list (YES in Step ST 42 ), the process goes to Step ST 43 . Alternatively, if similarity calculating section  21  determines that the characteristic point is present in the corresponding-point list (NO in Step ST 42 ), the process goes to Step ST 50 . 
         [0063]    Step ST 43  calculates the coordinate point of the affine-transformed characteristic point. Subsequent Step ST 44  determines whether the coordinate point acquired through affine transformation appears in the input image. If similarity calculating section  21  determines that the coordinate point acquired through affine transformation appears in the input image (YES in Step ST 44 ), the process goes to Step ST 45 . Alternatively, if similarity calculating section  21  determines that the coordinate point acquired through affine transformation does not appear in the input image (NO in Step ST 44 ), the process goes to Step ST 50 . 
         [0064]    Step ST 45  searches for a characteristic point in the input image closest to the coordinate point acquired through affine transformation. Subsequent Step ST 46  determines whether the distance in the coordinate space between the coordinates of the characteristic point in the input image searched for in Step ST 45  and the coordinate point acquired through affine transformation is smaller than or equal to a threshold. The threshold takes account of the error in the affine transformation. That means, if the result is positive in Step ST 46  (YES in Step ST 46 ), a characteristic point in the input image corresponding to the affine-transformed characteristic point in the reference image appears, and then, the process goes to Step ST 47 . In contrast, if the result is negative in Step ST 46  (NO in Step ST 46 ), a characteristic point in the input image corresponding to the affine-transformed characteristic point in the reference image does not appear even in consideration of the error in the affine transformation, and then, the process goes to Step ST 49 . 
         [0065]    Step ST 47  calculates the distance between characteristic values of the affine-transformed characteristic point in the reference image and the corresponding characteristic point in the input image. Then, Step ST 48  adds the coordinates of the characteristic points and the distance between the characteristic values to the corresponding-point list. 
         [0066]    Step ST 49  sets the coordinate point acquired through affine transformation as the coordinates of the characteristic point and adds the coordinates of the characteristic points and a sufficiently large distance between characteristic values to the corresponding-point list. The “sufficiently large distance between characteristic values” represents a value that can be identified as a varied region by subsequent varied-region identifying section  22 . 
         [0067]    Subsequent Step ST 50  determines whether the processing for all characteristic points in the reference image has been completed. If not completed (NO in Step ST 50 ), the process returns to Step ST 41  to repeat the same process for the next characteristic point. 
         [0068]    In this way, similarity calculating section  21  prepares a corresponding-point list. If any corresponding characteristic point exists between the input image and the reference image, a relatively small distance between characteristic values will be written in the corresponding-point list. In contrast, if no corresponding characteristic point exists between the input image and the reference image, a sufficiently large distance between characteristic values will be written in the corresponding-point list. A small distance between characteristic values indicates high similarity. 
         [0069]    On the basis of the corresponding-point list prepared by similarity calculating section  21 , varied-region identifying section  22  identifies a local region of aggregated characteristic points with large distances between characteristic values (i.e., low similarity) as a varied region. Specifically, varied-region identifying section  22  divides the input image into squares and casts a vote to each square that contains a characteristic point at which a distance between characteristic values calculated by similarity calculating section  21  is larger than or equal to a threshold. Similarity calculating section  21  repeats the voting process for all characteristic points and then identifies each square that received a number of votes larger than or equal to a threshold as a varied region. 
         [0070]    In this case, a varied region is identified by dividing the input image into squares and casting votes to each square, but the varied region may otherwise be detected by, for example, detecting a region of aggregated characteristic points at which distances between characteristic values are larger than or equal to a threshold. 
         [0071]      FIG. 7  shows images of the varied-region detection performed by varied-region detecting apparatus  10  according to this embodiment. 
         [0072]      FIG. 7A-1  illustrates an input image (captured image), and  FIG. 7B-1  illustrates a reference image.  FIG. 7A-2  illustrates characteristic points in the input image, and  FIG. 7B-2  illustrates characteristic points in the reference image.  FIG. 7B-3  illustrates the case where characteristic points with large distances between characteristic values appear in the reference image (that is, characteristic points that do not appear in the input image appear in the reference image). In such a case, varied-region identifying section  22  identifies the region defined by the thick frame in  FIG. 7A-3  as a varied (abnormal) region. That is, in the illustrated case, the document has been taken away. 
         [0073]    As described above, this embodiment includes characteristic-point selecting section  13  for corresponding-point search that selects characteristic points to be used for determination of a reference image from characteristic points in an input image (captured image); characteristic-point selecting section  18  for geometric-transformation-parameter calculation that selects characteristic points to be used for calculation of geometric transformation parameters from the characteristic points in the input image and the reference image; and characteristic-point selecting section  20  for similarity calculation that selects characteristic points to be used for determination of the similarity between the captured image and the reference image from the characteristic points in the input image and the reference image. Thereby, characteristic-point selecting sections  13 ,  18 , and  20  respectively and independently select characteristic points suitable for determination of reference image, characteristic points suitable for calculation of geometric transformation parameters, and characteristic points suitable for calculation of similarity. This enables the determination of an appropriate reference image, appropriate geometric transformation parameters, and accurate similarity, without excess calculation. As a result, a varied region can be accurately determined with a reduced calculation load. 
         [0074]    The entire content disclosed in the descriptions, drawings, and abstract of Japanese Patent Application No. 2010-172379, filed on Jul. 30, 2010, is hereby incorporated by reference. 
       INDUSTRIAL APPLICABILITY 
       [0075]    An apparatus and a method of detecting a varied region according to the present invention is suitable for, for example, a monitoring system that includes a wearable camera. 
       REFERENCE SIGNS LIST 
       [0000]    
       
           10  varied-region detecting apparatus 
           11  characteristic-point detecting section 
           12  characteristic-value calculating section 
           13  characteristic-point selecting section for corresponding-point search 
           14  corresponding-point searching section 
           15  characteristic-value index section 
           16  reference-image determining section 
           17  registered-image database 
           18  characteristic-point selecting section for geometric-transformation-parameter calculation 
           19  geometric-transformation-parameter calculating section 
           20  characteristic-point selecting section for similarity calculation 
           21  similarity calculating section 
           22  varied-region identifying section