Abstract:
A feature calculation unit ( 105 ) calculates the number of edge pixel pairs (three-dimensional histogram) defined by the edge direction of a predetermined pixel, the edge direction of an edge pixel present in a neighbor area of the predetermined pixel, and the spatial position relation between the predetermined pixel and the edge pixel present in the neighbor area of the predetermined pixel as a feature (S 5 ) of an image. With this, the feature (S 5 ) also represents the spatial relationship of the edges. Consequently, it is possible to also identify the difference of edge patterns due to the difference of the spatial relationships of the edges and increase the accuracy of identification of, for example, a portrait image.

Description:
TECHNICAL FIELD 
       [0001]    The present invention relates to an image feature extracting apparatus, object identification apparatus, and image feature extracting method, and relates, for example, to a technology that determines whether or not a provided grayscale image is an image of a human being. 
       BACKGROUND ART 
       [0002]    Conventionally, edge features are often used to classify whether a given grayscale image contains a human being. For example, a feature of histogram of edge directions HOG (Histograms of Oriented Gradients) is proposed for object identification (see Patent Literature 1 through 3, for example). 
         [0003]    For an grayscale image I of M×N(M: wide, N: height), suppose the number of edge directions is N i . A certain number of binary edge direction images E i  (i=1, . . . , N i ) are generated from I. Here, E i  is defined by the following equation. 
         [0000]      [1] 
         [0000]        E   i ( x,y )=1, if the edge direction at position ( x,y ) is  i.    
         [0000]        E   i ( x,y )=0, otherwise  (Equation 1)
 
         [0004]    Where i is the edge direction number, i=1, . . . , N i ,
       N i : Total number of edge direction       
 
         [0006]    An edge direction histogram (HOG) is defined by the following equation. 
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         [0007]    i: edge direction number, i=1, . . . , N i    
         [0008]    N i : total number of edge directions 
         [0009]    Using an edge direction histogram enables the edge image in  FIG. 1A  and the edge image in  FIG. 1B  to be discriminated. That is to say, using an edge direction histogram enables images with different edge directions to be discriminated. 
         [0010]    However, with an identification method using an edge direction histogram, statistical features of an entire image are calculated, and therefore discrimination between  FIG. 1B  and  FIG. 1C , for example, is difficult. This is because the accumulation of edge directions is the same in the edge image in  FIG. 1B  and the edge image in  FIG. 1C . As a result, false recognition occurs. 
         [0011]    Thus, the use of edge co-occurrence has been proposed as one method capable of differentiating between the edge image in  FIG. 1B  and the edge image in  FIG. 1C  (see Non-Patent Literature 1, for example). This method uses a co-occurrence matrix of edge direction as a feature indicating edge co-occurrence. Co-occurrence matrix of edge direction H CO (i,j) is represented by the following equation. 
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         [0012]    i, j: Edge direction number, i=1, . . . , N i , j=1, . . . , N j    
         [0013]    N i , N j : Total number of edge directions 
         [0014]    R(x, y): Neighboring area of (x, y) 
         [0015]    With an image identification method that uses edge direction co-occurrence, an edge direction within a neighboring area is taken into consideration in performing identification, and therefore correct identification is possible even for an edge image that cannot be correctly classified using only edge direction accumulation, as in the case of an edge direction histogram. 
       CITATION LIST 
     Patent Literature 
     PTL 1 
       [0000]    
       
         Japanese Patent Application Laid-Open No. HEIS-210739 
       
     
       PTL 2 
       [0000]    
       
         Japanese Patent Application Laid-Open No. 2003-263638 
       
     
       PTL 3 
       [0000]    
       
         Japanese Patent Application Laid-Open No. 2007-156626 
       
     
       Non-Patent Literature 
     NPL 1 
       [0000]    
       
         Sami Brand, Jorma Laaksonen, Erkki Oja, “Statistical Shape Features in Content-Based Image Retrieval”, Proceedings of the 15th International Conference on Pattern Recognition (ICPR&#39;2000), Volume 2, pp. 6062 
       
     
       SUMMARY OF INVENTION 
     Technical Problem 
       [0020]    However, with an image identification method that uses a conventional co-occurrence matrix of edge directions as described in Non-Patent Literature 1, the spatial relationship of edges is not sufficiently considered, and therefore there is a risk of false recognition of an object. With an image classification method that uses a conventional co-occurrence matrix of edge directions, it is difficult to differentiate between  FIG. 1C  and  FIG. 1D , for example. Incidentally, the kind of edge pattern shown in  FIG. 1D  often appears in an “elbow” of a human image, and the kind of edge pattern shown in  FIG. 1C  often appears in a “tree”. Therefore, when attempting to identify a pedestrian, for example, there is a possibility of false recognition of a “tree” image as an image of a pedestrian, which illustrates a deficiency of such a method. 
         [0021]    It is therefore an object of the present invention to provide a feature extracting apparatus, object identification apparatus, and feature extracting method capable of identifying a difference in edge patterns due to a difference in spatial relationships of edges, and capable of improving the accuracy of identification of a human image, for example. 
       Solution to Problem 
       [0022]    One aspect of a feature extracting apparatus of the present invention employs a configuration having a feature calculation section that calculates a number of edge pixels stipulated by an edge direction of a predetermined pixel and/or an edge direction of an edge pixel present in a neighboring area of the predetermined pixel, and a spatial relationship between the predetermined pixel and an edge pixel present in the neighboring area, as a feature of an image. 
         [0023]    One aspect of an object identification apparatus of the present invention employs a configuration having: the feature extracting apparatus; a plurality of weak classifiers that have a plurality of features obtained by the feature extracting apparatus as input, and output an estimate from an input feature and a classification function acquired by learning beforehand; a combining section that sums estimates output from the plurality of weak classifiers; and a determination section that performs a threshold value determination of a summed estimate. 
         [0024]    One aspect of a feature extracting method of the present invention includes: an edge image generating step of generating an edge image from a grayscale image; and a feature calculation step of calculating a number of edge pixels stipulated by an edge direction of a predetermined pixel and/or an edge direction of an edge pixel present in a neighboring area of the predetermined pixel, and a spatial relationship between the predetermined pixel and an edge pixel present in the neighboring area, as a feature of an image. 
       ADVANTAGEOUS EFFECTS OF INVENTION 
       [0025]    The present invention can identify a difference in edge patterns due to a difference in spatial relationships of edges, and can improve the accuracy of identification of a human image, for example. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0026]      FIG. 1  is a drawing showing examples of edge patterns; 
           [0027]      FIG. 2  is a block diagram showing the overall configuration of an object identification apparatus according to an embodiment of the present invention; 
           [0028]      FIG. 3  is a block diagram showing the configuration of a feature calculation section; 
           [0029]      FIG. 4A  is a drawing showing an example of an edge pattern that often appears in human images; 
           [0030]      FIG. 4B  is a drawing showing an example of an edge pattern that often appears in images of a tree; 
           [0031]      FIG. 5  is a drawing showing an example of edge directions; 
           [0032]      FIG. 6  is a drawing showing an example of neighboring area division; 
           [0033]      FIG. 7  is a block diagram showing the configuration of a identification section; and 
           [0034]      FIG. 8  is a flowchart provided to explain the operation of a feature calculation section and identification section. 
       
    
    
     DESCRIPTION OF EMBODIMENTS 
       [0035]    Now, an embodiment of the present invention will be described in detail with reference to the accompanying drawings. 
         [0036]    [Overall Configuration] 
         [0037]      FIG. 2  shows the overall configuration of an object identification apparatus according to an embodiment of the present invention. In this embodiment, a case will mainly be described in which object identification apparatus  100  identifies a human image, but an identification object of object identification apparatus  100  is not limited to a human image. 
         [0038]    Object identification apparatus  100  acquires an image by means of image acquisition section  101 . Image acquisition section  101  is a camera or scanner, for example. Image acquisition section  101  acquires an image, obtains grayscale image data S 1  by performing cropping of a necessary part and execution of preprocessing called image transformation on the acquired image, and outputs this grayscale image data S 1 . 
         [0039]    Noise removal section  102  removes noise from grayscale image data S 1 . In actuality, a noise removal filter can be used as noise removal section  102 , and a median filter, sharp filter, mean filter, or the like, can be used as a noise removal filter. Grayscale image data S 2  from which noise has been removed is output to image size normalization section  103 . 
         [0040]    Image size normalization section  103  performs size normalization of grayscale image data S 2  from which noise has been removed. That is to say, image size normalization section  103  changes the size of an input grayscale image to a predetermined size. For example, an enlargement operation is performed if the input image is smaller than the predetermined size, or a reduction operation is performed if the input image is larger than the predetermined size. In the example in this embodiment, a size of 64 (pixels)×128 (pixels) is used as an image normalization size, and all images are converted to a 64 (pixel)×128 (pixel) image size. Image data S 3  that has undergone size normalization is output to edge extraction section  104 . 
         [0041]    Edge extraction section  104  extracts an edge from grayscale image data S 3  that has undergone size normalization. Edge extraction section  104  obtains edge direction image data S 4  by differentiating the grayscale image data S 3 , for example. Edge direction image data S 4  is output to feature calculation section  105 . 
         [0042]    Feature calculation section  105  calculates feature S 5  from edge direction image data S 4 , and outputs this to identification section  106 . Based on feature S 5 , identification section  106  identifies whether or not an input image (that is, an image acquired by image acquisition section  101 ) is a human image. 
         [0043]    [Feature Calculation Section] 
         [0044]      FIG. 3  shows the configuration of feature calculation section  105 . Feature calculation section  105  inputs an edge direction image obtained by edge extraction section  104  to image cut-out section  105 - 1 . Image cut-out section  105 - 1  cuts out images of predetermined areas AR 1  and AR 2  from the edge image, as shown in  FIG. 4A  and  FIG. 4B .  FIG. 4A  and  FIG. 4B  show examples in which areas AR 1  and AR 2  are composed of 5 (pixels)×5 (pixels).  FIG. 4A  shows an example of an edge pattern that often appears in human images, and  FIG. 4B  shows an example of an edge pattern that often appears in images of a tree. Here, center pixel P 0  of areas AR 1  and AR 2  is a reference point for spatial relationships. 
         [0045]    A case in which area AR 1  in  FIG. 4A  is cut out is described below as an example. Area AR 1  internal edge image data is output to center edge pixel edge direction determination section  105 - 2 , neighboring edge pixel edge direction determination section  105 - 3 , and edge pixel spatial relationship detection section  105 - 4 . 
         [0046]    Here, in this embodiment, edge directions are divided into 6 directions, for example, as shown in  FIG. 5 . The ranges of edge directions  1  through  6  are as follows. 
         [0047]    Edge direction 1: 0° to 30°, 180° to 210° 
         [0048]    Edge direction 2: 30° to 60°, 210° to 240° 
         [0049]    Edge direction 3: 60° to 90°, 240° to 270° 
         [0050]    Edge direction 4: 90° to 120°, 270° to 300° 
         [0051]    Edge direction 5: 120° to 150°, 300° to 330° 
         [0052]    Edge direction 6: 150° to 180°, 330° to 360° 
         [0053]    The edge direction for a certain pixel is obtained by means of an arc tan computation of the difference (gradient) of brightness between that pixel and pixels horizontally and vertically adjacent to that pixel. If the difference in brightness is greater than a predetermined value, the edge direction of that pixel is taken as an obtained edge direction, and if the difference in brightness is less than or equal to the predetermined value, that pixel is determined not to be an edge image pixel, and the edge direction is taken to be −1. 
         [0054]    Center edge pixel edge direction determination section  105 - 2  finds the edge direction of center pixel P 0  (the coordinate (x, y) pixel) in  FIG. 4A , and outputs the obtained edge direction to computation section  105 - 5 . 
         [0055]    Neighboring edge pixel edge direction determination section  105 - 3  finds the edge direction of an edge pixel present in a neighboring area of center pixel P 0 , and outputs the obtained edge direction to computation section  105 - 5 . In the case of  FIG. 4A , four edge pixels are present in a neighboring area of center pixel P 0 , and therefore neighboring edge pixel edge direction determination section  105 - 3  determines the edge direction of each of these four edge pixels. 
         [0056]    In addition to this, feature calculation section  105  of this embodiment detects spatial relationships of edge pixels by means of edge pixel spatial relationship detection section  105 - 4 . Edge pixel spatial relationship detection section  105 - 4  detects in which division area, among division areas resulting from dividing a neighboring area of center pixel P 0  into a plurality of areas, an edge pixel present in a neighboring area of center pixel P 0  is present.  FIG. 6  shows an example of neighboring area division. In the example shown in  FIG. 6 , a neighboring area is divided into eight areas R 1  through R 8 . Edge pixel spatial relationship detection section  105 - 4  detects in which of areas R 1  through R 8  each edge pixel is located. 
         [0057]    Computation section  105 - 5  generates a three-dimensional histogram using detection results obtained by means of center edge pixel edge direction determination section  105 - 2 , neighboring edge pixel edge direction determination section  105 - 3 , and edge pixel spatial relationship detection section  105 - 4 . This three-dimensional histogram can be called a spatial co-occurrence matrix of edge directions. This spatial co-occurrence matrix of edge directions will now be described. 
         [0058]    Here, the number of edge directions is represented by N i , and a certain number of N i  (in the case of  FIG. 4A , six) edge direction images E i  are generated from provided M (pixel)×N (pixel) edge image S 4 . Here, E i  is defined by the following equation. 
         [0000]        E   i ( x,y )=1, if the edge direction at position ( x,y ) is  i.    
         [0000]        E   i ( x,y )=0, otherwise  (Equation 4)
 
         [0059]    Where i is the edge direction number, i=1, . . . , N i ,
       N i : Total number of edge directions       
 
         [0061]    Here, suppose the edge direction number of center pixel P 0  that is the reference point of spatial relationships is designated i, the edge direction number of a pixel present in a neighboring area of center pixel P 0  (in the case of  FIG. 4A  and  FIG. 4B , 24 pixels around center pixel P 0 ) is designated j, and the position number of a pixel present in a neighboring area of center pixel P 0  is designated k, computation section  105 - 5  generates spatial co-occurrence matrix of edge direction H SCO (i, j, k) by means of the following equation. 
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         [0062]    i,j: Edge direction number, i=1, . . . , N i , j=1, . . . , N j    
         [0063]    N i : Total number of edge directions 
         [0064]    k: Neighboring edge pixel position number, k=1, . . . , N k    
         [0065]    N k : Total number of division areas 
         [0066]    R k  in  FIG. 5  indicates a division area number, as shown in  FIG. 6 . 
         [0067]    Spatial co-occurrence matrix of edge directions H SCO (i, j, k) represented by equation 5 calculates a number of edge pixels stipulated by an edge direction of a predetermined pixel (center pixel P 0 ), an edge direction of an edge pixel present in a neighboring area of the predetermined pixel, and a spatial relationship between the predetermined pixel and an edge pixel present in the neighboring area, as a feature of an image. With spatial co-occurrence matrix of edge directions H SCO (i, j, k) represented by equation 5, edge spatial relationships are considered for an entire image, providing the capability of discrimination even of graphics for which discrimination is not possible by means of a conventional edge histogram (HOG) or a conventional co-occurrence matrix of edge directions. 
         [0068]    Here, for example, the value of H SCO (4, 3, 3) is the number of pixel pairs with edge direction 3 (60° to 90°, 240° to 270°) pixels in the top-right direction (position R 3 ) with respect to an edge direction 4 (90° to 120°, 270° to 300°) pixel at the center. In the edge pattern that often appears in images of a tree shown in  FIG. 4B , this value (feature) should be large. Specifically, in the case of the edge pattern in  FIG. 4B , H SCO (4, 3, 3)=2. In contrast, in the case of the edge pattern in  FIG. 4A , H SCO (4, 3, 3)=0. 
         [0069]    Also, the value of H SCO (4, 3 6,) is the number of pixel pairs with edge direction 3 (60° to 90°, 240° to 270°) pixels in the bottom-left direction (position R 6 ) with respect to an edge direction 4 (90° to 120°, 270° to 300°) pixel at the center. In the edge pattern that often appears in images of a human being shown in  FIG. 4A , this value (feature) should be large. Specifically, in the case of the edge pattern in  FIG. 4A , H SCO (4, 3 6,)=2. In contrast, in the case of the edge pattern in  FIG. 4B , H SCO (4, 3, 6)=0. 
         [0070]    In this way, computation section  105 - 5  generates spatial co-occurrence matrix of edge directions H SCO (i, j, k) for all combinations of i, j, and k, utilizing all edge image pixels. In the example described in this embodiment, i=1, 2, . . . , 6, j=1, 2, . . . , 6, and k=1, 2, . . . , 8, and therefore a spatial co-occurrence matrix of edge directions composed of 6×6×8=288 pixels is obtained. That is to say, 288 features (as a three-dimensional histogram) are obtained as features S 5 . 
         [0071]    [Identification Section] 
         [0072]      FIG. 7  shows the configuration of identification section  106 . Identification section  106  has plurality of weak classifiers  106 - 11  through  106 - 1   n , combining section  106 - 2 , and determination section  106 - 3 . 
         [0073]    Weak classifiers  106 - 11  through  106 - 1   n  have features S 5  (features  1  through n) obtained by feature calculation section  105  as input. The number of weak classifiers  106 - 11  through  106 - 1   n  provided corresponds to features  1  through n. In the example described in this embodiment, 288 weak classifiers are provided, but a smaller number than this may be utilized. 
         [0074]    Weak classifiers  106 - 11  through  106 - 1   n  using features acquired by means of learning beforehand, and a corresponding classification function. Specifically, when identification of a human being is attempted by means of object identification apparatus  100 , a large quantity of samples including human images and non-human images are used as training images in the learning process, output results S 5  of feature calculation section  105  of object identification apparatus  100  are obtained, and weak classifiers  106 - 11  through  106 - 1   n  compose classification functions corresponding to each feature of a human image by performing learning by means of a machine learning method—for example, a boosting method. 
         [0075]    Then, in actual image identification, estimates h 1  through h n  comprising human beings corresponding to the features are output, using features  1  through n input from feature calculation section  105  and the above classification functions acquired beforehand by means of learning. 
         [0076]    Estimates h 1  through h n  are summed by combining section  106 - 2 , and estimate H obtained thereby is output to determination section  106 - 3 . Determination section  106 - 3  performs threshold value determination of estimate H, and if estimate H is greater than a predetermined threshold value, determines that the input image is a human image, and outputs determination result S 6 . 
         [0077]    [Operation] 
         [0078]    The operation of object identification apparatus  100  will now be described using  FIG. 8 . Here, the operation of feature calculation section  105  and identification section  106 , which are features of the present invention, will be described in particular. 
         [0079]    After object identification apparatus  100  starts feature calculation and identification processing in step ST 10 , in step ST 11 , center edge pixel edge direction determination section  105 - 2 , neighboring edge pixel edge direction determination section  105 - 3 , and edge pixel spatial relationship detection section  105 - 4  in feature calculation section  105  detect the edge direction of each edge pixel and edge spatial relationships for all the pixels of an image. 
         [0080]    Then, in step ST 12 , computation section  105 - 5  of feature calculation section  105  calculates a spatial co-occurrence matrix of edge directions by means of equation 5 as features S 5 . 
         [0081]    Then, in step ST 13 , corresponding features that are elements of the spatial co-occurrence matrix of edge directions (features  1  through n) are input to corresponding weak classifiers  106 - 11  through  106 - 1   n.    
         [0082]    Then, in step ST 14 , weak classifiers  106 - 11  through  106 - 1   n  calculate estimates h i  (where i=1 through n), and in step ST 15 , combining section  106 - 2  calculates estimate H. 
         [0083]    Then, in step ST 15 , determination section  106 - 3  performs threshold value determination of estimate H. If estimate H is greater than a threshold value, determination section  106 - 3  proceeds to step ST 17 , determines that the input image is a human image, and outputs “1” as determination result S 6 . On the other hand, if estimate H is less than or equal to the threshold value, determination section  106 - 3  proceeds to step ST 18 , determines that the input image is a non-human image, and outputs “−1” as determination result S 6 . 
         [0084]    After step ST 17  or step ST 18 , feature calculation and identification processing is terminated in step ST 19 . 
         [0085]    [Effects] 
         [0086]    As described above, according to this embodiment, feature calculation section  105  can obtain feature S 5  that also represents a spatial relationship of edges by calculating as feature S 5  of an image a number of edge pixels (three-dimensional histogram) stipulated by an edge direction of a predetermined pixel (in this embodiment, a center pixel), an edge direction of an edge pixel present in a neighboring area of the predetermined pixel, and a spatial relationship to an edge pixel present in a neighboring area of the predetermined pixel. As a result, a difference in edge patterns due to a difference in spatial relationships of edges can be identified, and the accuracy of identification of a human image, for example, can be improved. 
         [0087]    Also, a spatial co-occurrence matrix of edge directions (feature) calculated by this embodiment is a global feature, enabling a feature to be obtained that is robust with respect to positional displacement or posture variation of a human being in an image. 
       OTHER EMBODIMENTS 
       [0088]    In the above embodiment, a case has been described in which calculation as a feature is performed of a number of edge pixels (three-dimensional histogram) stipulated by an edge direction of a predetermined pixel, an edge direction of an edge pixel present in a neighboring area of the predetermined pixel, and a spatial relationship to an edge pixel present in a neighboring area of the predetermined pixel, but, for example, a number of edge pixels (two-dimensional histogram) stipulated by an edge direction of a predetermined pixel, and a spatial relationship to an edge pixel present in a neighboring area of the predetermined pixel, may also be calculated as a feature of an image. 
         [0089]    In this case, the configuration of feature calculation section  105  can be made a configuration in which neighboring edge pixel edge direction determination section  105 - 3  is omitted from the configuration in  FIG. 3 , and computation section  105 - 5  can generate spatial co-occurrence matrix of edge directions H 1 ( i,k ) by means of the following equation. 
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                                   j 
                                   = 
                                   1 
                                 
                                 
                                   N 
                                   i 
                                 
                               
                                
                               
                                 
                                   ∑ 
                                   
                                     
                                       ( 
                                       
                                         
                                           x 
                                           ′ 
                                         
                                         , 
                                         
                                           y 
                                           ′ 
                                         
                                       
                                       ) 
                                     
                                     ∈ 
                                     
                                       
                                         R 
                                         k 
                                       
                                        
                                       
                                         ( 
                                         
                                           x 
                                           , 
                                           y 
                                         
                                         ) 
                                       
                                     
                                   
                                 
                                  
                                 
                                   
                                     E 
                                     j 
                                   
                                    
                                   
                                     ( 
                                     
                                       
                                         x 
                                         ′ 
                                       
                                       , 
                                       
                                         y 
                                         ′ 
                                       
                                     
                                     ) 
                                   
                                 
                               
                             
                           
                           ] 
                         
                       
                     
                   
                 
               
               
                 
                   [ 
                   6 
                   ] 
                 
               
             
           
         
       
     
         [0090]    i: Edge direction number, i=1, . . . , N i    
         [0091]    N i : Total number of edge directions 
         [0092]    k: Edge pixel position number, k=1, . . . , N k    
         [0093]    N k : Total number of division areas 
         [0000]    In this case, identification accuracy will probably be lower than in the above embodiment, but a feature whereby a spatial relationship of edges is represented can be obtained in the same way as in the above embodiment, and therefore a difference in edge patterns due to a difference in spatial relationships of edges can be identified. 
         [0094]    Also, a number of edge pixels (two-dimensional histogram) stipulated by an edge direction of an edge pixel present in a neighboring area of a predetermined pixel, and a spatial relationship to an edge pixel present in a neighboring area of the predetermined pixel, may also be calculated as a feature of an image. 
         [0095]    In this case, the configuration of feature calculation section  105  can be made a configuration in which center edge pixel edge direction determination section  105 - 2  is omitted from the configuration in  FIG. 3 , and computation section  105 - 5  can find spatial co-occurrence matrix of edge directions H 2 ( j,k ) by means of the following equation. 
         [0000]    
       
         
           
             
               
                 
                   ( 
                   
                     Equation 
                      
                     
                         
                     
                      
                     7 
                   
                   ) 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     
                       H 
                       2 
                     
                      
                     
                       ( 
                       
                         j 
                         , 
                         k 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       
                         N 
                         j 
                       
                     
                      
                     
                       
                         1 
                         NM 
                       
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                           ∑ 
                           
                             x 
                             = 
                             0 
                           
                           
                             M 
                             - 
                             1 
                           
                         
                          
                         
                           
                             ∑ 
                             
                               y 
                               = 
                               0 
                             
                             
                               N 
                               - 
                               1 
                             
                           
                            
                           
                             [ 
                             
                               
                                 
                                   E 
                                   i 
                                 
                                  
                                 
                                   ( 
                                   
                                     x 
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                                     y 
                                   
                                   ) 
                                 
                               
                                
                               
                                 
                                   ∑ 
                                   
                                     
                                       ( 
                                       
                                         
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                                           ′ 
                                         
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                                           y 
                                           ′ 
                                         
                                       
                                       ) 
                                     
                                     ∈ 
                                     
                                       
                                         R 
                                         k 
                                       
                                        
                                       
                                         ( 
                                         
                                           x 
                                           , 
                                           y 
                                         
                                         ) 
                                       
                                     
                                   
                                 
                                  
                                 
                                   
                                     E 
                                     j 
                                   
                                    
                                   
                                     ( 
                                     
                                       
                                         x 
                                         ′ 
                                       
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                                         y 
                                         ′ 
                                       
                                     
                                     ) 
                                   
                                 
                               
                             
                             ] 
                           
                         
                       
                     
                   
                 
               
               
                 
                   [ 
                   7 
                   ] 
                 
               
             
           
         
       
     
         [0096]    j: Edge direction number, j=1, . . . , N i    
         [0097]    N j : Total number of edge directions 
         [0098]    k: Edge pixel position number, k=1, . . . , N k    
         [0099]    N k : Total number of division areas 
         [0000]    In the above embodiments, a case has been described in which identification section  106  is configured as having plurality of weak classifiers  106 - 11  through  106 - 1   n , but the configuration of identification section  106  is not limited to this. The present invention has, as a particular feature, feature calculation processing by feature calculation section  105 , and therefore other configuration elements may be changed arbitrarily. 
         [0100]    The disclosure of Japanese Patent Application No. 2008-288864, filed on Nov. 11, 2008, including the specification, drawings and abstract, is incorporated herein by reference in its entirety. 
       INDUSTRIAL APPLICABILITY 
       [0101]    The present invention has an effect of enabling a difference in edge patterns due to a difference in spatial relationships of edges to be identified, and is suitable for identifying a human image, for example.