Patent Publication Number: US-8983204-B2

Title: Image processing apparatus and method for controlling same

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to an image processing apparatus and to a method for controlling this apparatus. 
     2. Description of the Related Art 
     There are conventional image processing apparatus with which the contour (edge) of an image is detected and contour enhancement processing is performed according to the characteristics of the contour. Also known is an image processing apparatus with which the type of subject in an image is determined, and image processing is performed according to the determined type of subject. 
     For example, Japanese Patent Laid-Open No. 2000-59651 discloses an image processing apparatus with which the degree of contour enhancement is controlled according to the brightness level of an input signal. With the technique of Japanese Patent Laid-Open No. 2000-59651, the contour enhancement is weakened for video signal portions with a low brightness level, and is strengthened for video signal portions with a high brightness level. Also, Japanese Patent Laid-Open No. 2002-190984 discloses an image processing apparatus which determines whether an image is a landscape image or a text image, and if it is a text image, performs control to strengthen the contour enhancement. 
     With the technique disclosed in Japanese Patent Laid-Open No. 2000-59651, however, because the degree of contour enhancement is controlled on the basis of just the brightness of the contour to be corrected, contour enhancement will be performed to the same degree as long as the brightness is the same, no matter what the contour portion of a subject is. As a result, depending on the subject, the contour enhancement may be too strong, or conversely it may be insufficient, which is a problem in that the proper processing cannot be performed adequately on the subject. 
     With the technique disclosed in Japanese Patent Laid-Open No. 2002-190984, the type of image (whether it is a text image or a landscape image) is determined according to the brightness distribution. However, Japanese Patent Laid-Open No. 2002-190984 does not disclose a discrimination technique that is suited to discriminating between anything other than a text image and a landscape image (such as discriminating between a manmade object such as a building and a natural object such as a flower or a landscape). Even if an attempt was made to utilize the technique of Japanese Patent Laid-Open No. 2002-190984 to discriminate between manmade and natural objects, it would still be difficult to discriminate with high accuracy because no clear characteristic difference is obtained in relation to the brightness distribution between manmade and natural objects. Therefore, it is difficult to change the degree of contour enhancement between buildings and natural objects, for example. 
     SUMMARY OF THE INVENTION 
     The present invention was conceived in light of this situation, and provides a technique that allows the type of subject included in an image to be determined with high accuracy. 
     According to an aspect of the present invention, there is provided an image processing apparatus, comprising: an acquisition unit configured to acquire image data expressing an image including a specific subject; a production unit configured to produce a contour signal expressing a contour portion included in the image; a detection unit configured to detect, on the basis of the contour signal, a representative contour direction for each of a plurality of division regions obtained by dividing up the image, the detection unit detecting a specific direction as the representative contour direction when the direction of the entire contour portion included in the division regions is biased in the specific direction by at least a specific degree; a determination unit configured to determine a type of the subject on the basis of at least one of the following: a direction-based frequency distribution of the detected representative contour directions, a degree to which the division regions in which a representative contour direction of a predetermined direction was detected are continuously aligned in a direction perpendicular to the predetermined direction in the image, and a number of representative contour directions detected; and a correction unit configured to correct the image data according to a correction method corresponding to the type of the subject. 
     According to another aspect of the present invention, there is provided a method for controlling an image processing apparatus, comprising: an acquisition step of acquiring image data expressing an image including a specific subject; a production step of producing a contour signal expressing a contour portion included in the image; a detection step of detecting, on the basis of the contour signal, a representative contour direction for each of a plurality of division regions obtained by dividing up the image, wherein a specific direction is detected as the representative contour direction when the direction of the entire contour portion included in the division regions is biased in the specific direction by at least a specific degree; a determination step of determining a type of the subject on the basis of at least one of the following: a direction-based frequency distribution of the detected representative contour directions, a degree to which the division regions in which a representative contour direction of a predetermined direction was detected are continuously aligned in a direction perpendicular to the predetermined direction in the image, and a number of representative contour directions detected; and a correction step of correcting the image data according to a correction method corresponding to the type of the subject. 
     With the above constitution of the present invention, it is possible to determine with high accuracy the type of subject included in an image. 
     Further features of the present invention will become apparent from the following description of exemplary embodiments with reference to the attached drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an example of the configuration of a digital camera  100  according to a first embodiment. 
         FIG. 2  is a detail view of an image processing unit  105 . 
         FIGS. 3A to 3G  are diagrams of examples of contour enhancement processing with a contour enhancement processing unit  206 . 
         FIG. 4A  is a diagram of an example of an input signal. 
         FIG. 4B  is a diagram of an example of a filter. 
         FIG. 4C  is a diagram of an example of a vertical BPF  221 . 
         FIG. 4D  is a diagram of an example of a horizontal BPF  222 . 
         FIG. 4E  is a diagram of an example of an upper-right BPF  223 . 
         FIG. 4F  is a diagram of an example of a lower-right BPF  224 . 
         FIG. 5A  is a diagram of an example of dividing up an image with a signal dividing unit  250  in the first embodiment. 
         FIG. 5B  is a diagram of an example of dividing up an image with the signal dividing unit  250  in a second embodiment when the zoom is to the telephoto side or the focal distance is at or below a specific threshold. 
         FIG. 5C  is a diagram of an example of dividing up an image with the signal dividing unit  250  in the second embodiment when the zoom is to the wide angle side. 
         FIG. 6A  is a flowchart giving an overview of subject type determination processing, and image correction processing according to the type of subject, in the first embodiment. 
         FIG. 6B  is a flowchart of the details of the processing in step S 602  in  FIG. 6A . 
         FIG. 7  is a flowchart of the details of the processing in step S 611  in  FIG. 6B . 
         FIG. 8  is a diagram of an example of block classification. 
         FIG. 9A  is a diagram of a subject and how block division is done. 
         FIG. 9B  is a diagram in which the representative contour directions (contour portions) of the blocks corresponding to  FIG. 9A  are indicated by lines. 
         FIG. 9C  is a diagram of an example of contour direction connectivity. 
         FIGS. 10A and 10B  are graphs of the distribution by subject when various characteristics related to contour are plotted. 
         FIGS. 11A and 11B  are tables showing examples of parameters of image correction processing corresponding to various subjects. 
         FIG. 12  is a flowchart of the details of processing in step S 603  in  FIG. 6A  in a modification example of the first embodiment. 
         FIGS. 13A to 13D  are diagrams illustrating the grouping (labeling) of blocks. 
         FIG. 14  is a flowchart of the details of processing in step S 602  in  FIG. 6A  in the second embodiment. 
         FIGS. 15A to 15C  are concept diagrams of correction of the contour direction according to the tilt of the digital camera  100  in the second embodiment. 
         FIG. 16  is a flowchart of the details of processing in step S 611  in  FIG. 6B  in a third embodiment. 
         FIG. 17  is a flowchart of the details of processing in step S 603  in  FIG. 6A  in a fourth embodiment. 
         FIGS. 18A to 18D  are concept diagrams of processing to determine whether or not there is a building in the background of a face in the fourth embodiment. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     Embodiments of the present invention will now be described with reference to attached drawings. It should be noted that the technical scope of the present invention is defined by claims, and is not limited by each embodiment described below. In addition, not all combinations of the features described in the embodiments are necessarily required for realizing the present invention. 
     First Embodiment 
     An embodiment in which the image processing apparatus of the present invention is applied to a digital camera will now be described through reference to  FIGS. 1 to 5A  and  6 A to  13 D.  FIG. 1  is a block diagram of an example of the configuration of a digital camera  100  according to a first embodiment. 
     In  FIG. 1 ,  101  is a lens group that includes a zoom lens and a focus lens,  102  is a shutter having an aperture function, and  103  is an image capturing unit constituted by a CCD, a CMOS element, or the like that converts an optical image into an electrical signal.  104  is an A/D converter that converts an analog signal into a digital signal, and  105  is an image processing unit that performs various kinds of image correction processing, such as gamma processing, color correction processing, contour enhancement processing, or noise reduction processing, on image data outputted from the A/D converter  104 .  106  is an image memory,  107  is a memory control unit that controls the image memory  106 ,  108  is a D/A converter that converts an inputted digital signal into an analog signal,  109  is an LCD or other such display unit, and  110  is a codec unit that subjects image data to compression coding and decoding. 
       180  is a memory card, hard disk, or other such recording medium,  111  is an interface with the recording medium  180 , and  50  is a system control unit that controls the entire system of the digital camera  100 . 
       120  is a manipulation unit for inputting various operational commands,  121  is a power switch,  122  is a power control unit, and  123  is a power supply. The power control unit  122  is made up of a battery detection circuit, a DC-DC converter, a switching circuit for switching the blocks to which current is supplied, and so forth, and is used to detect whether a battery has been installed, the type of battery, and the remaining battery charge. The power control unit  122  also controls the DC-DC converter on the basis of these detection results and a command from the system control unit  50 , and supplies the required voltage to the various units of the digital camera  100 , including the recording medium  180 , as long as required. 
       124  is a nonvolatile memory capable of electrical deletion and recording, and is an EEPROM or the like, for example.  125  is a gyro acceleration sensor that detects the tilt and acceleration of the digital camera  100 .  126  is a system timer that keeps track of time used in various kinds of control, or the time on a built-in clock, and  127  is a system memory that expands programs and the like read from the nonvolatile memory  124 , as well as constants and variables used in the operation of the system control unit  50 . 
     Next, the basic operation during subject capture with the digital camera  100  configured as above will be described. The image capturing unit  103  subjects light that is incident through the lens group  101  and the shutter  102  to opto-electronic conversion, and outputs the product to the A/D converter  104  as an input image signal. The A/D converter  104  converts the analog image signal outputted from the image capturing unit  103  into a digital image signal, and outputs the product to the image processing unit  105 . 
     The image processing unit  105  subjects image data from the A/D converter  104 , or image data from the memory control unit  107 , to white balance processing or other such color conversion processing, and to gamma processing, contour enhancement processing, noise reduction processing, and so forth, which will be described later. The image processing unit  105  also uses captured image data to perform specific calculation processing, and the system control unit  50  performs exposure control and metering control on the basis of the calculated result thus obtained. Consequently, TTL (through-the-lens)-style AF (auto focus) processing, AE (auto exposure) processing, and EF (electronic pre-flash) processing are performed. The image processing unit  105  further uses captured image data to perform specific calculation processing, and also performs TTL-style AWB (auto white balance) processing on the basis of the calculated result thus obtained. 
     The image data outputted from the image processing unit  105  is written to the image memory  106  via the memory control unit  107 . The image memory  106  holds image data outputted from the image capturing unit  103 , and image data for display on the display unit  109 . 
     The D/A converter  108  converts the display-use image data held in the image memory  106  into an analog signal, and supplies it to the display unit  109 . The display unit  109  performs display according to the analog signal from the D/A converter  108  on an LCD or other display device. 
     The codec unit  110  subjects the image data recorded to the image memory  106  to compression coding according to a standard such as MPEG. The system control unit  50  stores the coded image data in the recording medium  180  via the interface  111 . 
     The above is the basic operation during subject capture. In addition to the basic operation discussed above, the system control unit  50  also performs the various processing of this embodiment discussed below by executing programs recorded to the above-mentioned nonvolatile memory  124 . The word program as used in this embodiment means a program for executing the processing of the various flowcharts discussed below. The system control unit  50  here expands constants and variables used for the operation of the system control unit  50 , as well as programs and so forth read from the nonvolatile memory  124 , in the system memory  127 . 
     Next, the image processing unit  105  will be described in detail through reference to  FIG. 2 . In  FIG. 2 ,  200  is a brightness/color signal production unit,  201  is a WB amplification unit  201 ,  202  is a color gamma processing unit,  203  is a color difference signal production unit,  204  is a color correction unit, and  230  is a noise reduction unit.  205  is a brightness gamma processing unit, and  206  is a contour enhancement processing unit. In the contour enhancement processing unit  206 ,  207  is a bandpass filter (BPF),  208  is a coring unit,  209  is a gain processing unit,  210  is a clip processing unit, and  211  is an addition processing unit.  220  is a direction-specific contour signal production unit, in which  221  is a BPF for the vertical direction,  222  is a BPF for the horizontal direction,  223  is a BPF for the upper-right (diagonal) direction, and  224  is a BPF for the lower-right (diagonal) direction.  250  is a signal dividing unit. 
     Next, the processing performed by the image processing unit  105  will be described. The image data inputted from the A/D converter  104  or the memory control unit  107  in  FIG. 1  is inputted to the image processing unit  105 . The image data inputted to the image processing unit  105  is then inputted to the brightness/color signal production unit  200 . The brightness/color signal production unit  200  produces a brightness signal Y and color signals R, G, and B from the inputted image data. The color signals R, G, and B are outputted to the WB amplification unit  201 , while the brightness signal Y is outputted to the brightness gamma processing unit  205  and the direction-specific contour signal production unit  220 . 
     The WB amplification unit  201  adjusts the white balance by applying gain to the color signals R, G, and B on the basis of the white balance gain values calculated by the system control unit  50 . The color gamma processing unit  202  performs gamma correction on the color signals R, G, and B. The color difference signal production unit  203  produces color difference signals R-Y and B-Y from the color signals R, G, and B, and outputs these to the color correction unit  204 . The color correction unit  204  then adjusts the hue and saturation by applying gain to the color difference signals R-Y and B-Y, for example. The color correction unit  204  outputs the corrected color difference signals R-Y and B-Y to the noise reduction unit  230  and the signal dividing unit  250 . 
     Meanwhile, the brightness gamma processing unit  205  performs gamma correction on the brightness signal Y, and outputs the product to the contour enhancement processing unit  206 .  FIGS. 3A to 3G  are diagrams of examples of contour enhancement processing at the contour enhancement processing unit  206 .  FIG. 3A  shows a portion of the brightness signal (horizontal signal) inputted to the contour enhancement processing unit  206 . 
     The BPF  207  is a bandpass filter that extracts a specific frequency band. The original inputted signal ( FIG. 3A ) is subjected to bandpass filtering to obtain the contour signal shown in  FIG. 3B . The output signal from the BPF  207  is outputted to the coring unit  208 . As shown in  FIG. 3G , the coring unit  208  performs clip processing on a tiny portion of the input portion. As a result, the input signal has the shape shown in  FIG. 3C . The output signal from the coring unit  208  is inputted to the gain processing unit  209 . The gain processing unit  209  applies gain to the input signal. The signal obtained by applying gain to the signal in  FIG. 3C  is shown in  FIG. 3D . The output signal from the gain processing unit  209  is inputted to the clip processing unit  210 . The clip processing unit  210  clips an input signal that is higher (or lower) than a specific limit level to the limit level. An example of this clipping is shown in  FIG. 3E . The output signal from the clip processing unit  210  is outputted to the addition unit  211 . The addition unit adds the original signal ( FIG. 3A ) to the contour signal ( FIG. 3E ) outputted from the clip processing unit  210 , and produces a brightness signal ( FIG. 3F ) with an enhanced contour. The contour enhancement processing unit  206  outputs the brightness signal Y whose contour has been enhanced as discussed above to the noise reduction unit  230  and the signal dividing unit  250 . Only contour enhancement in the horizontal direction was described above, but the same contour enhancement processing is performed in the vertical direction. 
     Next, the processing of the noise reduction unit  230  will be described. The noise reduction unit  230  performs spatial noise reduction processing. More specifically, it performs processing with an ε filter. An epsilon filter uses the differential between the pixel of interest to be processed and the surrounding pixels, as local information. If this differential is less than the ε value, a low-pass filter is applied. When the ε filter is expressed as a one-dimensional signal, it is as follows. 
     
       
         
           
             
               
                 
                   
                     
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     Here, a k  is the filter coefficient of the low-pass filter, and is designed so that the sum will be one. 
     The noise reduction unit  230  outputs the brightness signal Y and the color difference signals R-Y and B-Y that have undergone noise reduction processing as above to the image memory  106  via the memory control unit  107 . 
     Next, the processing of the direction-specific contour signal production unit  220  will be described. The direction-specific contour signal production unit  220  applies the four kinds of BPF in parallel to the inputted brightness signal Y, and outputs the four kinds of image signal (contour signal) that have passed through the BPFs to the signal dividing unit  250 . The direction-specific contour signal production unit  220  uses a contour detecting filter (BPF) of 3×3 pixels in each contour direction to extract the contour components (contour signals) for the horizontal direction, vertical direction, upper-right direction, and lower-right direction from the brightness signal Y. 
     The contour detecting filters (BPFs  221  to  224 ) will be described through reference to  FIGS. 4A to 4F .  FIG. 4A  shows an input signal f (i, j), and  FIG. 4B  shows the filter applied to the input signal f (i, j). The output signal f′ (i, j) produced by filtering is calculated from the following equation. 
     
       
         
           
             
               
                 
                   
                     
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     The contour component for each direction can be extracted by varying the filter coefficient in the above equation.  FIGS. 4C to 4F  show examples of filter coefficients for detecting the contour in each direction.  FIG. 4C  is an example of a vertical contour (horizontal line) detecting filter used by the vertical BPF  221 , and  FIG. 4D  is an example of a horizontal contour (vertical line) detecting filter used by the horizontal BPF  222 .  FIG. 4E  is an example of a diagonal (lower-right line) detecting filter used by the upper-right BPF  223 , and  FIG. 4F  is an example of a diagonal (upper-right line) detecting filter used by the lower-right BPF  224 . 
     As discussed above, the direction-specific contour signal production unit  220  produces an image signal (contour signal) expressing the contour portion for each direction included in an image, and outputs this signal to the signal dividing unit  250 . 
     The signal dividing unit  250  divides the inputted image signal into a plurality of division regions (8×8 blocks) shown in  FIG. 5A , and performs signal computation for each block. In this embodiment, the brightness signal Y, the color difference signals R-Y and B-Y, and a contour signal from the direction-specific contour signal production unit  220  are inputted to the signal dividing unit  250 . The signal dividing unit  250  calculates the average value for each block from the brightness signal Y and the color difference signals R-Y and B-Y. The signal dividing unit  250  also calculates the sum for each block from the contour signals outputted from the direction-specific contour signal production unit  220 . The more contour components there are included in a block, the greater is the sum, so the sum can be used as an indicator of the quantity of contour components included in the block. Since contour signals related to four directions are outputted from the direction-specific contour signal production unit  220 , the sum is also calculated for each of the four directions. The data calculated by the signal dividing unit  250  is stored in the system memory  127 . 
     The image processing unit  105  was described in detail above. Next, processing to determine the type of subject and image correction processing according to the type of subject according to the first embodiment will be described through reference to the flowcharts in  FIGS. 6A ,  6 B, and  7 . When the power to the digital camera  100  is switched on and the apparatus is ready for image capture, the processing in the flowchart of  FIG. 6A  begins. 
     In step S 602 , the system control unit  50  determines the type of subject included in an input image. In this embodiment, the system control unit  50  identifies whether the subject is a building or a natural object (such as a flower or a landscape). Details of the processing in step S 602  will be described through reference to  FIG. 6B . 
     In step S 610 , the image processing unit  105  produces contour signals from the inputted image for each of the four directions (vertical, horizontal, upper-right, and lower-right) by the method discussed above, calculates the sum of the contour signals in each direction for every block, and stores the result in the system memory  127 . The system control unit  50  acquires from the system memory  127  the sum of the contour signals for every block and every direction thus calculated. 
     In step S 611 , the system control unit  50  classifies the 8×8 blocks according to the characteristics of the contour, on the basis of the sum of the contour signals acquired in step S 610 , and detects a representative contour direction for every block. The “representative contour direction” is a representative direction related to the entire contour portion included in the block, and if the direction of the entire contour portion is biased in a specific direction by at least a specific degree, then this specific direction is detected as the representative contour direction. Therefore, no representative contour direction is detected when there is no pronounced bias in the direction of the entire contour portion, or when not very much of the contour portion is included (when the sum of the contour signals is small). Details of the processing in step S 611  will be described through reference to  FIG. 7 . 
     In step S 701 , the system control unit  50  begins loop processing on the 8×8 blocks one at a time. 
     In step S 702 , the system control unit  50  determines whether or not at least one of the sums of the contour signals in the four directions (vertical, horizontal, upper-right, and lower-right) of the block being processed is greater than a threshold TH 1 . If the value is greater than TH 1 , the processing proceeds to step S 703 , and otherwise the processing proceeds to step S 709 . 
     In step S 703 , the system control unit  50  determines whether or not at least two of the sums of the contour signals in the four directions (vertical, horizontal, upper-right, and lower-right) of the block being processed are greater than a threshold TH 2  (where TH 2 &gt;TH 1 ). If these values are greater than TH 2 , the processing proceeds to step S 707 , and otherwise the processing proceeds to step S 704 . 
     In step S 704 , the system control unit  50  detects the maximum value from among the sums of the contour signals in the four directions (vertical, horizontal, upper-right, and lower-right) of the block being processed. This maximum value shall be termed E 1 . 
     In step S 705 , the system control unit  50  acquires the sum of the contour signals in a direction perpendicular to the direction of the maximum value detected in step S 704 . This sum shall be termed E 2 . For example, if the direction of the maximum value is the horizontal direction (that is, if the contour is pronounced in the horizontal direction (vertical line)), then the direction that is perpendicular will be the vertical direction (direction of horizontal line). Similarly, if the direction of the maximum value is the upper-right direction (that is, if the contour is pronounced in the upper-right direction (lower-right line)), then the direction that is perpendicular will be the lower-right direction (direction of upper-right line). 
     In step S 706 , the system control unit  50  determines whether or not the contour strength in the direction of the maximum value is sufficiently high compared to the contour strength of the direction that is perpendicular. More specifically, for example, the system control unit  50  determines whether or not E 1 &lt;k*E 2  (k&lt;1). If E 1 &lt;k*E 2 , then the processing proceeds to step S 708 , and otherwise the processing proceeds to step S 707 . 
     In step S 707 , the system control unit  50  classifies the block being processed as a “directionless block (complex block).” A “directionless block” is a block that includes many contour signals within the block, but in which the contour signals are not biased in a specific direction. In a directionless block, no representative contour direction will be detected.  FIG. 8  is a diagram of an example of block classification. A block that includes contour signals in various directions as in block  801 , or a block that includes contour signals in two perpendicular directions as in block  802  is classified as a directionless block. 
     In step S 708 , the system control unit  50  classifies the block being processed as a “contour block,” and detects the direction of the maximum value detected in step S 704  as the representative contour direction. The system control unit  50  records the detected representative contour direction to the system memory  127 . A “contour block” is a block having a distinct contour portion in only a specific direction, as with the block  803  in  FIG. 8 . 
     In step S 709 , the system control unit  50  classifies the block being processed as a contourless block.” A contourless block is a flat block that has no contour portion, as with the blocks  804  and  805  in  FIG. 8 . No representative contour direction is detected in a contourless block. 
     As discussed above, classification of blocks and detection of the representative contour direction are carried out for the 8×8 blocks one at a time. Also, as discussed above, the system control unit  50  records the detected representative contour direction for “contour blocks” in the system memory  127 . 
     Returning to  FIG. 6B , in step S 612  the system control unit  50  begins loop processing of the 8×8 blocks one at a time. 
     In step S 613 , the system control unit  50  determines whether or not the block being processed is a “contour block” (a block for which a representative contour direction was detected). If it is a “contour block,” the processing proceeds to step S 614 , and otherwise (if it is a “directionless block” or a “contourless block”) the processing returns to step S 612  and the next block to be processed is selected. 
     In step S 614 , the system control unit  50  calculates the contour direction connectivity of the contour (that is, the contour of a vertical line) in a specific direction (the horizontal direction here). The calculation of the contour direction connectivity will be described in detail through reference to  FIGS. 9A to 9C . 
       FIG. 9A  is a diagram of a subject and how block division is done. Here, we will describe a case of calculating the contour direction connectivity of a block  901 . To calculate the contour direction connectivity of a block with vertical lines, the system control unit  50  first determines whether or not the representative contour direction of the block being processed is the horizontal direction (vertical line). If it is the horizontal direction (vertical line), the system control unit  50  counts the number of blocks in which the representative contour direction is the horizontal direction (vertical line) that are continuously aligned in a direction perpendicular to the representative contour direction (that is, the up and down direction of that block), and this count shall be termed the contour direction connectivity.  FIG. 9B  is a diagram in which the representative contour directions of the blocks corresponding to  FIG. 9A  (contour portions) are indicated by lines (these lines are lines in a direction perpendicular to the representative contour direction). If the contour direction connectivity of the block  901  (the block being processed) is calculated here, the system control unit  50  counts how many blocks having a representative contour direction in the horizontal direction (vertical line) are continuously aligned, in relation to the upper and lower blocks adjacent to the block  901 . In the example of the block  901 , since there are in total three blocks aligned continuously, with one above and two below, the contour direction connectivity of the block  901  is 3. Similarly, when the contour direction connectivity in the horizontal direction (vertical line) is calculated for all the blocks, the result is as shown in  FIG. 9C . The system control unit  50  finally calculates the sum for contour direction connectivity. This sum is an indicator of the degree to which blocks for which a specific representative contour direction (the horizontal direction here) was detected are aligned continuously in a direction perpendicular to this representative contour direction (the vertical direction here) in the inputted image. 
     In step S 615 , the system control unit  50  increments the counter of the representative contour direction for the block being processed (prior to step S 612 , the counter of each direction is initialized at zero). This processing ultimately yields the number of blocks for each representative contour direction. 
     As discussed above, whether or not a block is a “contour block” is determined for the 8×8 blocks one at a time, and if a block is a “contour block,” then calculation of the contour direction connectivity and counting of the number of blocks for each representative contour direction are performed. 
     After this, in step S 616 , the system control unit  50  determines the type of subject on the basis of at least one of the following: the direction-specific frequency distribution of the representative contour direction (that is, the number of blocks counted for each representative contour direction in step S 615 ), the sum for contour direction connectivity, and the number of detected representative contour directions (that is, the number of “contour blocks”). The specific combination of this information (characteristics related to contour) can be suitably varied according to the type of subject determined, the required determination precision, and so on. Here, as one example, a case of determining whether the subject is a building or a natural object will be described through reference to  FIGS. 10A and 10B . 
       FIGS. 10A and 10B  are graphs of the distribution by subject when various characteristics related to contour are plotted. In this embodiment, buildings are differentiated from natural objects (flowers or landscapes), so the distribution of buildings, flowers, and landscapes is shown. In  FIG. 10A , the horizontal axis shows the number of blocks having a representative contour direction in the horizontal or vertical direction, and the vertical axis shows the number of blocks having a representative contour direction in a diagonal direction (upper-right or lower-right). In  FIG. 10B , the horizontal axis shows the contour direction connectivity of the horizontal direction (vertical line), and the vertical axis shows the proportion (or number) of “contour blocks.” 
     The system control unit  50  decides where the subject included in an inputted image is present in the graphs of  FIGS. 10A and 10B  on the basis of the sum of contour direction connectivity calculated in step S 614  and the number of blocks for each representative contour direction counted in step S 615  (the number of blocks for each representative contour direction is added as needed). For example, let us consider a case in which a certain subject is present at the locations of the characteristic point  1001  in  FIG. 10A  and the characteristic point  1002  in  FIG. 10B . 
     Next, referring to  FIG. 10A , and considering a specific axis A 1  that separates two types of subject (a building and something else) and an X 1  axis that is perpendicular to the axis A 1 , the point at which the characteristic point  1001  of the subject is projected on the X 1  axis is calculated, and this point shall be termed X 1   p . Referring to  FIG. 10B , similarly, we will consider axes X 2  and X 3  perpendicular to specific axes A 2  and A 3  that separate types of subject. Points at which the characteristic point  1002  is projected onto the axes X 2  and X 3  are similarly X 2   p  and X 3   p.    
     An evaluation value is calculated on the basis of the following equation from the values of Xlp, X 2   p , and X 3   p  found as above.
 
evaluation value=α X 1 p+βX 2 p+γX 3 p  
 
Here, α, β, and γ are preset weighting coefficients.
 
     If the evaluation value is at or above a specific threshold, the system control unit  50  determines the type of subject to be a “building,” and otherwise determines the type of subject to be a “natural object.” 
     The processing of  FIG. 6B  (the processing of step S 602  in  FIG. 6A ) was described above. Returning to  FIG. 6A , in step S 603  the image processing unit  105  performs image correction processing according to the type of subject (image correction processing by a correction method corresponding to the type of subject) under the control of the system control unit  50 . More specifically, for example, the system control unit  50  sets the parameters of the contour enhancement processing unit  206  and the noise reduction unit  230  to values corresponding to the type of subject. The contour enhancement processing unit  206  and the noise reduction unit  230  execute contour enhancement processing and noise reduction processing according to the set parameters. An example of the parameters in image correction processing corresponding to various kinds of subject will be described through reference to  FIG. 11A . 
     If the type of subject is a “building,” then it is possible that image quality will be better with the contour strongly enhanced. In view of this, as shown in  FIG. 11A , the center frequency of contour detection is set between medium and low. This is done by varying the parameter (coefficient) of the BPF  207  in  FIG. 2 . Also, the coring range is expanded in the coring unit  208 . Specifically, in  FIG. 3G , the range over which the input values are clipped is expanded. Also, the gain processing unit  209  is controlled to increase the gain of contour enhancement. Furthermore, the E value is increased in the noise reduction unit  230 . 
     On the other hand, if the type of subject is a “natural object,” it will look strange if the contour is enhanced too much, so it is better not to enhance the contour very strongly. Since the reproduction of fine detail is important in a natural object, it is best if there is not much loss of information about detail in coring and noise reduction. In view of this, the center frequency of contour detection is set between medium and high by varying the parameter of the BPF  207 . The range of coring is set narrow in the coring unit  208 , and the gain of the gain processing unit  209  is set weak. Also, the ε value is reduced in the noise reduction unit  230 . 
     The processing in step S 603  in  FIG. 6A  (image correction processing according to the type of subject) was described above. Next, in step S 604 , the system control unit  50  determines whether or not the processing should be ended. For instance, it is determined that processing should be ended if the power to the digital camera  100  has been switched off. If the processing is not ended, the flow returns to step S 601  and the same processing is repeated. 
     As described above, in the first embodiment, the digital camera  100  divides an image into a plurality of blocks, detects the representative contour direction for each block, and determines the type of subject on the basis of the detection results (frequency distribution, contour direction connectivity, number of representative contour directions detected, etc.). Also, the digital camera  100  executes image correction processing according to a correction method corresponding to the type of subject. 
     Thus, by detecting the representative contour direction in block units, it is possible to detect characteristics related to the contour of a subject, and the result will be less apt to be affected by subject noise or fine patterns. Consequently, it is possible to determine with high accuracy the type of subject included in an image. As a result, it is possible to execute contour enhancement processing, noise reduction processing, or other such image correction processing that is suited to the type of subject. Also, since the representative contour direction is detected in block units, the computation load is reduced as compared to when a contour is detected in pixel units. 
     Furthermore, in this embodiment, processing by the contour enhancement processing unit  206  and processing by the noise reduction unit  230  were given as an example of image correction processing according to the type of subject, but the image correction processing is not limited to these. For instance, brightness gamma processing or color correction processing may be changed according to the type of subject. In this case, for a “building,” the system control unit  50  controls the brightness gamma processing unit  205  to execute brightness gamma processing so as to strengthen the contrast. For a “natural object,” the system control unit  50  controls the color correction unit  204  to execute color correction processing so as to raise the saturation. 
     Also, in this embodiment, only processing using an ε filter, which is a spatial filter, was described as processing by the noise reduction unit  230 , but the method for noise reduction is not limited to this. For instance, a time filter that performs low-pass processing in the time direction may be used, in which case the parameter of the time filter may be varied according to the type of subject. 
     Also, in this embodiment, an example was discussed in which a “building” was distinguished from a “natural object,” but as long as the representative contour direction is detected in block units, and the type of subject is determined using the frequency distribution, the contour direction connectivity, the number of representative contour directions detected, and so forth, then any type of subject may be determined. 
     Also, in this embodiment, the type of subject was determined as a binary choice between “building” and “natural object,” but it does not necessarily have to be a binary determination. For instance, the higher is the evaluation value calculated in the example of  FIGS. 10A and 10B , the greater the likelihood that the subject is a “building,” so the parameter of image correction processing may be set toward building as the evaluation value rises, and set toward natural object as the evaluation value drops. 
     Also, in this embodiment, the classification of blocks (whether or not they have a contour) was decided on the basis of the sum of the contour signals, by direction, for the block being processed, but the block classification method is not limited to this. For instance, a method may be employed in which blocks are classified by using a brightness signal and a color signal in addition to a contour signal. In this case, the system is controlled so that a block is not determined to be a contour block if the brightness is above or below a specific threshold. Also, a color signal may be used to determine whether or not a block is a contour block or to detect a representative contour direction. In this case, if the average values for hue and saturation within a block satisfy a specific condition, then the block is determined not to be a contour block (or the condition for determining the representative contour direction is changed). More specifically, a block having many green signals of high saturation is not determined to be a contour block because of the low probability that the contour is of a building. Alternatively, even if a block is determined to be a contour block, the threshold for determining it to be a block having a horizontal or vertical contour is changed to make it less likely that it will be determined to have a horizontal or vertical direction. This makes it possible to further raise the accuracy of determining the type of subject. 
     Also, the block being processed may be compared with surrounding blocks, and determined to be a contour block if the sum of contour signals is large with respect to that of surrounding blocks. Here, the sum of contour signals is compared with that of adjacent blocks in the same direction as the contour direction. For example, if a contour block is detected in the horizontal direction (vertical line), the sum of contour signal is compared with that of the left and right adjacent blocks, which are in the same direction (the horizontal direction), and the block is determined to be a contour block if a larger differential in the sum of contour signals from that of the left and right blocks is at or above a specific threshold. 
     Modification Example 
     In the first embodiment, the image correction method varied with the type of subject, but correction was performed using the same parameters for the entire image. However, correction may be performed using parameters corresponding to the characteristics of different regions of an image. This will now be described through reference to  FIG. 11B ,  FIG. 12 , and  FIGS. 13A to 13D . 
       FIG. 12  is a flowchart of the details of processing in step S 603  in  FIG. 6A  in a modification example of the first embodiment. 
     In step S 1200 , the system control unit  50  determines whether or not the type of subject determined in step S 602  is a “building.” If it is a “building,” the processing proceeds to step S 1201 , and otherwise the processing proceeds to step S 1211 . 
     Steps S 1201  to S 1210  are executed when the subject has been determined to be a “building,” and the location and characteristics of the building (whether it is a building with many flat portions, such as an office building, or is a building with intricate details, such as a church) are determined. More specifically, in step S 1201 , the system control unit  50  labels the 8×8 blocks on the basis of the similarity among the blocks, and thereby groups the blocks. Details of this labeling will be described through reference to  FIGS. 13A to 13D . 
       FIG. 13A  shows a captured subject image that has been divided into blocks. The system control unit  50  subjects these blocks to labeling while raster scanning the search range shown in  FIG. 13B . In  FIG. 13B , the block  1301  is the block being processed, and the blocks to the upper-left, above, to the upper-right, and to the left of the block being processed shall be termed reference blocks (shaded). 
     The system control unit  50  compares the block being processed with the reference blocks for brightness and color, and gives the block being processed the same label as that of the reference block with the closest brightness and color. For example, in  FIG. 13C , a label of “2” is given to the block to the upper-left of the block being processed  1302 , and a label of “3” is given to the blocks above, to the upper-right, and to the left. If at this point the reference block that is closest in brightness and color to the block being processed  1302  is the upper-left block, then the system control unit  50  gives a label of “2” to the block being processed  1302 . If, however, there is no reference block, or if the difference between the block being processed and all of the reference blocks is over a specific value, the system control unit  50  gives a new label to the block being processed. 
     Thus, the system control unit  50  labels all of the blocks by similar regions.  FIG. 13D  is an example of labeling all the blocks of the subject in  FIG. 13A . As a result of this labeling, the 8×8 blocks are grouped so that there will be at least a specific similarity among the blocks within a group (among division regions). The search range is not limited to that is shown in  FIG. 13B . In  FIG. 13D , the blocks in a group are arranged continuously, but depending on the setting of the search range, blocks belonging to the same group may be present at separated locations. 
     Returning to  FIG. 12 , in step S 1202  the system control unit  50  begins loop processing on the labels given in step S 1201 , one at a time (in the example shown in  FIG. 13D , the labels “0” to “3” are processed sequentially). 
     In step S 1203 , the system control unit  50  determines whether or not the size of the group of the label being processed (the number of blocks in the group) is at or above a specific threshold. If the size is at or above the specific threshold, the processing proceeds to step S 1204 , and otherwise the processing proceeds to step S 1209 . 
     In step S 1204 , the system control unit  50  calculates the proportion of blocks having a representative contour direction in the horizontal or vertical direction (vertical and horizontal lines) with respect to all of the “contour blocks” in the group of the label being processed. The system control unit  50  also calculates the proportion of “contourless blocks” (blocks in which the contour signals are at or below a specific amount, as can be understood from steps S 702  and S 709  in  FIG. 7 ) with respect to all the blocks in the group of the label being processed. 
     In step S 1205 , the system control unit  50  determines whether or not the proportion of blocks having a representative contour direction in the horizontal or vertical direction (vertical and horizontal lines) calculated in step S 1204  is at or above a threshold. If it is at or above the threshold, the processing proceeds to step S 1206 , and otherwise the processing proceeds to step S 1209 . 
     In step S 1206 , the system control unit  50  determines whether or not the proportion of “contourless blocks” calculated in step S 1204  is at or above a threshold. If it is at or above the threshold, the processing proceeds to step S 1207 , and otherwise the processing proceeds to step S 1208 . 
     In step S 1207 , the system control unit  50  associates a flat building flag with the label being processed. This flag indicates that the subject in the group of the label being processed is a building having many flat surfaces, such as on an office building. 
     In step S 1208 , the system control unit  50  associates a complex building flag with the label being processed. This flag indicates that the subject in the group of the label being processed is a building having many intricate details, such as on a church. 
     In step S 1209 , the system control unit  50  associates a non-building label with the label being processed. 
     In step S 1210 , the image processing unit  105  performs image correction processing according to the type of subject in a group (image correction processing by a correction method corresponding to the type of subject in a group), for each group, under the control of the system control unit  50 . The processing of step S 1210  will be described in specific terms through reference to  FIG. 11B .  FIG. 11B  is similar to  FIG. 11A  in that it shows an example of types of subjects and corresponding parameters for contour enhancement processing and noise reduction processing.  FIG. 11B  differs from the  FIG. 11A  in that when the type of subject is a “building,” the parameters are stipulated for every type of subject within a group. 
     In the example in  FIG. 11B , when the subject in a group is a flat building, the same parameters are used as with the “building” in  FIG. 11A . On the other hand, in the case of a complex building, the center frequency of contour detection is set to between low and medium. Also, the coring range is narrowed, the gain is set to medium, and the ε value of noise reduction processing is reduced. This makes it possible to execute contour enhancement processing and noise reduction processing while minimizing the loss of information about details for buildings having intricate designs, such as a church. Furthermore, for non-building regions, the parameters are set to be the same as when the subject is a “natural object.” 
     Returning to  FIG. 12 , in step S 1211  the image processing unit  105  executes image correction processing corresponding to a “natural object” under the control of the system control unit  50 . This processing is the same as when the subject is a “natural object” in step S 603  of the first embodiment. 
     A case was described above of performing image correction processing for each group according to the type of subject in a group. In this modification example, a case in which the image processing parameters were set for every group in block units was described as an example, but if the image processing parameters are switched in block units, a step may appear at a group boundary that is attributable to the difference in image processing parameters. Accordingly, processing to reduce this step may be added, such as using a median value of two parameters at the group boundary. 
     Second Embodiment 
     A second embodiment of the present invention will now be described through reference to  FIGS. 5B and 5C , and  14  to  15 C. In this second embodiment, the type of subject is determined by substantially the same processing as in the first embodiment, but the determination processing also includes the state (zoom, focus, orientation, etc.) of the digital camera  100  during capture of an image. The configuration and basic operation of the digital camera  100  in the second embodiment are the same as those described for the first embodiment, and will therefore not be described in detail again (see  FIGS. 1 and 2 ). 
     In the second embodiment, the details of the processing in step S 602  in  FIG. 6A  differ from those in the first embodiment, and the processing of  FIG. 14  is executed instead of that in  FIG. 6B . In  FIG. 14 , those blocks in which the same processing is performed as in  FIG. 6B  are numbered the same, and will not be described again. 
     In step S 1401 , the system control unit  50  acquires information indicating the current zoom and focus states from the lens group  101 , and stores them in the system memory  127 . 
     In step S 1402 , the system control unit  50  produces camera tilt information from the output of the gyro acceleration sensor  125 , and stores this in the system memory  127 . 
     In step S 1403 , the system control unit  50  changes the block division size and number in the signal dividing unit  250  (see  FIG. 2 ) on the basis of the zoom information and focus information acquired in step S 1401 . More specifically, the system control unit  50  increases the block size when the zoom is toward the telephoto end (when the zoom magnification is large) or when the focal distance is shorter than a specific threshold, and decreases the block size when the zoom is toward the wide angle end. Specifically, when the focal distance is shorter than a specific threshold, the block size is at least a specific size regardless of the zoom magnification. This example is shown in  FIGS. 5B and 5C .  FIG. 5B  shows the block division (8×8 blocks in this example) when the zoom is toward the telephoto end or when the focal distance is shorter than a specific threshold.  FIG. 5C , meanwhile, shows the block division (16×16 blocks in this example) when the zoom is toward the wide angle end. In this case, there are more blocks because the block size is reduced and information from the entire image is acquired. Controlling the system like this makes it possible to prevent the zoom from causing large fluctuations in the amount of information related to the subject shape and so forth for a single block. 
     In step S 1405 , the system control unit  50  uses the tilt information produced in step S 1402  to correct the direction of the contour. More specifically, the system control unit  50  corrects the contour direction when the digital camera  100  is tilted by at least a specific threshold.  FIG. 15A  shows an image captured in a state in which the digital camera  100  was tilted by more than a threshold (such as 30 degrees). Here, the direction of the contour is corrected according to the direction of tilt. 
       FIG. 15B  shows the contour direction before correction, and  FIG. 15C  shows the contour direction after correction. That is, the system control unit  50  corrects so that a contour signal outputted as a horizontal contour (vertical line)  1501   a  is treated as a lower-right contour  1501   b , and a contour signal outputted as an upper-right contour  1502   a  is treated as a horizontal contour (vertical line)  1502   b . Similarly, the system control unit  50  corrects so that a contour signal outputted as a vertical contour (horizontal line)  1503   a  is treated as an upper-right contour  1503   b , and a contour signal outputted as a lower-right contour  1504   a  is treated as a vertical contour (horizontal line)  1504   b . Thus, only direction information is corrected according to the tilt. 
     The processing in step S 1406  is the same as in step S 614  in  FIG. 6B , but the processing is slightly different when tilt is corrected in step S 1405 . For example, when the contour direction is corrected as shown in  FIG. 15C , the horizontal contour (vertical line) is connected diagonally, the contour direction connectivity is calculated in a diagonal direction. In other words, in calculating the contour direction connectivity, connection in a direction perpendicular to the representative contour direction in question (the horizontal direction here) is evaluated, but this “perpendicular direction” is also corrected according to the correction of the contour direction. 
     The processing in step S 1407  is the same as in step S 616  in  FIG. 6B , but the threshold for distinguishing whether the subject is a “building” or a “natural object” (A 1  to A 3  in  FIGS. 10A and 10B ) is changed on the basis of focus information. More specifically, the threshold is changed (A 1  to A 3  in  FIGS. 10A and 10B ) so that the subject will be less likely to be determined to be a “building” if the focal distance is less than a specific distance. For example, processing is performed to shift the axis A 2  upward and to shift the axis A 3  to the right. 
     As described above, in the second embodiment, the digital camera  100  performs processing to determine the type of subject by including the state of zoom, tilt, and so forth during image capture. This makes it possible to increase determination accuracy over that when the type of subject is determined from the image alone. 
     In this embodiment, a case was described in which the image processing apparatus of the present invention was applied to the digital camera  100 , but this embodiment can also be applied to an apparatus having no image capture system, so long as it performs image processing. In this case, zoom and other such camera information is added to the image data ahead of time, and processing is performed by the image processing apparatus on the basis of this. 
     Also, in this embodiment an example was described in which the number (size) of block divisions of signal division involving control depending on the state of the zoom and focus was two types, but the number of divisions of blocks is not limited to two types. A configuration can be employed in which the size or number of divisions of blocks is controlled in multiple steps according to the characteristics of the zoom. 
     Third Embodiment 
     A third embodiment of the present invention will now be described through reference to  FIG. 16 . This third embodiment differs from the first embodiment in that the type of subject is determined by using characteristics about the movement over time of the subject and the digital camera  100 . The configuration and basic operation of the digital camera  100  in the third embodiment are the same as those described for the first embodiment, and will therefore not be described in detail again (see  FIGS. 1 and 2 ). 
     In this third embodiment, the details of the processing in step S 611  in  FIG. 6B  differ from those in the first embodiment, and the processing of  FIG. 16  is executed instead of that in  FIG. 7 . In  FIG. 16 , those blocks in which the same processing is performed as in  FIG. 7  are numbered the same, and will not be described again. 
     In step S 1601 , the system control unit  50  calculates a frame difference value in relation to the block being processed. More specifically, the system control unit  50  calculates pixel difference values for block images at the same position of an earlier image signal (second image data expressing a second image), which is stored in step S 1605  described later, and calculates the sum of these pixel difference values within a group. 
     In step S 1602 , the system control unit  50  acquires the acceleration of the digital camera  100  from the output of the gyro acceleration sensor  125 . The system control unit  50  determines that the digital camera  100  is moving if the acceleration is at or above a specific threshold TH 3 . If the digital camera  100  is determined to be moving the processing proceeds to step S 702 , and otherwise the processing proceeds to step S 1603 . 
     In step S 1603 , the system control unit  50  determines whether or not the sum of frame difference values calculated in step S 1601  is greater than a threshold TH 4 . If the sum is greater than the threshold TH 4 , the processing proceeds to step S 1604 , and otherwise the processing proceeds to step S 702 . 
     In step S 1604 , the system control unit  50  determines that the block being processed is a non-building block. This is because a region that is moving even though the digital camera  100  is not moving is considered not to be a building. In other words, a moving region is considered to be a person region, or a region of a natural object such as a plant that is swaying in the wind. In this case no representative contour direction is detected. 
     In step S 1605 , the system control unit  50  records the image currently being processed to the image memory  106 . This image is utilized in calculating the frame difference values in step S 1601 . 
     Block classification processing based on movement information (detection processing of representative contour direction) was described above. Processing other than this is the same as the processing described through reference to  FIGS. 6A and 6B  in the first embodiment, and therefore will not be described again. 
     In this embodiment, processing in which blocks for subject regions that are moving over time are not considered to be contour blocks is added to the first embodiment. This allows just the desired subject contour to be detected more accurately, so the type of subject is determined more accurately. 
     Also, in this embodiment a case of distinguishing between a building and a natural object was described as an example, but as long as the region used for discrimination is limited by movement of the subject, any kind of subject can be distinguished. 
     In this embodiment, subject movement information was utilized only in processing to determine the type of subject, but the parameters for image correction processing by the image processing unit  105  may be changed on the basis of movement. For instance, processing may be added so that if the subject is determined to be a natural object, the gain of contour enhancement processing is increased and the median frequency is lowered in moving regions as compared to stationary regions. This makes it possible to avoid enhancing a contour unnaturally by performing contour enhancement processing of the high-frequency component with respect to a subject for which high-frequency information has been lost due to movement. 
     Fourth Embodiment 
     A fourth embodiment of the present invention will be described through reference to  FIG. 17  and  FIGS. 18A to 18D . In this fourth embodiment, substantially the same image correction processing is performed as in the first embodiment, but the image correction processing includes the positional relation between a person and the subject. The configuration and basic operation of the digital camera  100  in the fourth embodiment are the same as those described for the first embodiment, and will therefore not be described in detail again (see  FIGS. 1 and 2 ). 
     This fourth embodiment differs from the first embodiment in the details of the processing in step S 603  of  FIG. 6A , and instead the processing of  FIG. 17  is executed. In step S 1701 , the system control unit  50  groups (labels) blocks and determines the type of subject in a group by the same processing as in steps S 1201  to S 1209  in  FIG. 12 . 
     In step S 1702 , the system control unit  50  detects a human face in the image. Any known method may be used for this facial detection processing. For example, pattern matching is performed with the feature amount for a face and the feature amount for a specified region in the image, and it is determined to be a face if the degree of matching is at or above a specific threshold. 
     In step S 1703 , the system control unit  50  determines whether or not a face was detected in step S 1702 . If a face was detected, the processing proceeds to step S 1704 , and otherwise the processing proceeds to step S 1707 . 
     In step S 1704 , the system control unit  50  determines whether or not the type of subject determined in step S 602  ( FIG. 6A ) is a “building.” If it is a “building,” the processing proceeds to step S 1705 , and otherwise the processing proceeds to step S 1707 . 
     In step S 1705 , the system control unit  50  determines whether or not a building is present in the background of a detected face (background determination). This processing will be described through reference to  FIGS. 18A to 18D .  FIG. 18A  shows a captured image, and  FIG. 18B  shows an example of labeling corresponding to  FIG. 18A . Here, a flag for a building region (a flat building flag or complex building flag in step S 1207  or S 1208 ) is associated with the label “1” by labeling processing. The region of the label “2” is a region in which a face was detected in step S 1702 . In this step, the system control unit  50  determines whether or not a block including a label for a building region is adjacent to a block including a face region. In the example in  FIG. 18B , a block with a label of “2” is adjacent to a block with a building label of “1,” so a building is determined to be present in the background of a face. Meanwhile,  FIG. 18C  shows a captured image just as in  FIG. 18A , and  FIG. 18D  shows an example of labeling corresponding to  FIG. 18C . A label of “1” corresponds to a building region, and a label of “2” corresponds to a face region. In  FIG. 18D , since no block with a label of “1” is adjacent to a block with a label of “2,” it is determined that no building is present in the background of a face. If it is determined by this determination processing that a building is present in the background of a face, the processing proceeds to step S 1706 , and otherwise the processing proceeds to step S 1707 . 
     In step S 1706 , the image processing unit  105  executes image correction processing for a background building under the control of the system control unit  50 . In image correction processing for a background building, basically the parameters according to the type of subject shown in  FIG. 11A  are used. However, the gain is set lower in the contour enhancement processing unit  206 . 
     Meanwhile, in step S 1707 , the image processing unit  105  executes image correction processing with the parameters shown in  FIG. 11A , just as in the first embodiment, under the control of the system control unit  50 . 
     In this embodiment, the digital camera  100  changed the parameters for image correction processing depending on whether or not a face is detected and a building is included in the background of the face. Consequently, it is possible to prevent excessive enhancement of the contour of a background subject (in this case, a building) that would make enhance it more than a face. 
     Furthermore, in this embodiment an example was discussed in which only the gain in contour enhancement processing was changed as the image correction processing for a background building, but the parameters that are changed are not limited to this. For instance, the ε value of noise reduction processing may be increased if a building is present in the background of a face, or any other parameter may be changed. 
     In addition to the control discussed above, it is also possible to have a configuration in which camera information is used to change a parameter. For example, when the lens group  101  is set to a state that will blur the background (open aperture, etc.), processing may be performed to switch off contour enhancement processing other than for a face, and strengthen noise reduction. This makes it possible to avoid enhancing the contour even though the background is blurred, which would produce an unnatural image. Also, a building was used as an example in this embodiment, but the present invention can be similarly applied when the subject is any manmade object having contour features similar to those of a building, such as a table or other furniture. 
     Other Embodiments 
     Aspects of the present invention can also be realized by a computer of a system or apparatus (or devices such as a CPU or MPU) that reads out and executes a program recorded on a memory device to perform the functions of the above-described embodiments, and by a method, the steps of which are performed by a computer of a system or apparatus by, for example, reading out and executing a program recorded on a memory device to perform the functions of the above-described embodiments. For this purpose, the program is provided to the computer for example via a network or from a recording medium of various types serving as the memory device (e.g., computer-readable medium). 
     While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions. 
     This application claims the benefit of Japanese Patent Application No. 2011-262657, filed on Nov. 30, 2011, which is hereby incorporated by reference herein in its entirety.