Patent Publication Number: US-9898680-B2

Title: Feature image generation apparatus, classification apparatus and non-transitory computer-readable memory, and feature image generation method and classification method

Description:
INCORPORATION BY REFERENCE 
     This application is a continuation of U.S. application Ser. No. 14/986,978, filed on Jan. 4, 2016, which claims the benefit of Japanese Application No. 2015-007444, filed on Jan. 19, 2015. The disclosures of the applications referenced above are incorporated herein by reference in their entireties. 
    
    
     BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention relates to image processing. 
     Description of the Background Art 
     The technology of classifying whether an object corresponds to a comparison target using image processing has conventionally been proposed. For example, Japanese Patent No. 5317250 discloses the technology of classifying, using image processing, whether a component being a test target that appears in an image corresponds to a genuine component, namely, a non-defective item. 
     SUMMARY OF THE INVENTION 
     A feature image generation apparatus includes circuitry. The circuitry generates, on the basis of a processing target image in which an object appears, a first image showing the object; and generates, as a feature image showing a feature of the object, at least a part of a rotational composite image obtained by composition of a plurality of rotated images obtained by rotating the first image. 
     A classification apparatus includes circuitry. The circuitry generates, on the basis of a processing target image in which an object appears, a first image showing the object; generates, as a feature image showing a feature of the object, at least a part of a first rotational composite image obtained by composition of a plurality of first rotated images obtained by rotating the first image, and classifies, on the basis of the feature image, whether the object appearing in the processing target image corresponds to a comparison target. 
     A non-transitory computer-readable memory stores a control program for causing a computer to generate, on the basis of a processing target image in which an object appears, a feature image showing a feature of the object. The control program causes the computer to execute (a) generating, on the basis of the processing target image in which an object appears, a first image showing the object; and (b) generating, as the feature image showing a feature of the object, at least a part of a rotational composite image obtained by composition of a plurality of rotated images obtained by rotating the first image. 
     A non-transitory computer-readable memory stores a control program for causing a computer to classify whether an object appearing in a processing target image corresponds to a comparison target. The control program causes the computer to execute: (a) generating, on the basis of a processing target image in which an object appears, a feature image showing a feature of the object, and (b) classifying, on the basis of the feature image, whether the object appearing in the processing target image corresponds to a comparison target. The control program causes, in the step (a), the computer to execute (a-1) generating, on the basis of the processing target image, a first image showing the object, and (a-2) generating, as the feature image, at least a part of a rotational composite image obtained by composition of a plurality of rotated images obtained by rotating the first image. 
     A feature image generation method includes (a) generating, on the basis of a processing target image in which an object appears, a first image showing the object, and (b) generating, a second image as the feature image showing a feature of the object, at least a part of a rotational composite image obtained by composition of a plurality of rotated images obtained by rotating the first image. 
     A classification method classifies whether an object appearing in a processing target image corresponds to a comparison target. The classification method includes (a) generating, on the basis of a processing target image in which an object appears, a feature image showing a feature of the object, and (b) classifying, on the basis of the feature image, whether the object appearing in the processing target image corresponds to a comparison target. The step (a) includes (a-1) generating, on the basis of the processing target image, a first image showing the object, and (a-2) generating, as the feature image, at least a part of a rotational composite image obtained by composition of a plurality of rotated images obtained by rotating the first image. 
     These and other objects, features, aspects and advantages of the present invention will become more apparent from the succeeding detailed description of the present invention when taken in conjunction with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a configuration of an image processing system; 
         FIG. 2  schematically illustrates an example of a captured image; 
         FIG. 3  illustrates a configuration of a classification apparatus; 
         FIG. 4  illustrates a configuration of a feature image generation unit; 
         FIG. 5  is a flowchart illustrating an action of the classification apparatus; 
         FIG. 6  schematically illustrates an example of a coin region; 
         FIG. 7  schematically illustrates an example of a background image; 
         FIG. 8  schematically illustrates an example of a background subtraction image; 
         FIG. 9  schematically illustrates an example of an outline template: 
         FIG. 10  is a diagram for explaining template matching; 
         FIG. 11  is a diagram for explaining processing of extracting a coin region; 
         FIG. 12  schematically illustrates an example of an edge image; 
         FIG. 13  is a diagram for explaining a method of generating a rotational composite image; 
         FIG. 14  is a diagram for explaining a method of generating a template feature image; and 
         FIGS. 15 to 18  are diagrams for explaining a method of generating a feature image. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     &lt;Configuration of Image Processing System  1 &gt; 
       FIG. 1  illustrates a configuration of an image processing system  1  according to an embodiment. The image processing system  1  according to this embodiment is a system that uses image processing to classify whether an object corresponds to a comparison target. The image processing system  1  is provided in a vending machine that accepts, for example, a circular coin. The image processing system  1  classifies whether a coin inserted from the outside into the vending machine in operation corresponds to a genuine one. In other words, the image processing system  1  classifies whether a coin inserted from the outside to the vending machine in operation is a genuine one, that is, the authenticity of the coin. In this embodiment, accordingly, an object is a coin. A comparison target to be compared with the object is a genuine coin (authentic coin). The object may not be a coin. The outline of the object may not be circular. 
     As illustrated in  FIG. 1 , the image processing system  1  includes an imaging apparatus  2  and a classification apparatus  3 . The imaging apparatus  2  captures an image of a coin inserted into the vending machine to generate a captured image  10  in which the coin appears, and then outputs the captured image  10  to the classification apparatus  3 . In the vending machine, a coin moves while rotating on a rail, and the imaging apparatus  2  captures an image of the rotating coin. In this embodiment, the captured image  10  generated by the imaging apparatus  2  is a color image, which may be a gray scale image. 
       FIG. 2  schematically illustrates an example of a captured image  10  obtained by the imaging apparatus  2 . A genuine coin  100 , that is, an authentic coin  100  appears in the captured image  10  illustrated in  FIG. 2 . Also appearing in the captured image  10  is a rail  101  being a background. For example, an alphabet “A” pattern appears on a main surface  100   a  of the authentic coin  100 . The authentic coin  100  may have any other pattern. The authentic coin  100  may have patterns on both main surfaces. 
     In this embodiment, the coin  100  moves on the rail  101  while rotating about the axis of rotation passing through the center between the main surfaces of the coin  100  and extending in the thickness direction. The imaging apparatus  2  captures an image of the coin  100  from the main surface  100   a  side of the coin  100 . The main surface  100   a  of the coin  100  accordingly appears in the captured image  10 . 
     The classification apparatus  3  classifies whether the coin  100  appearing in the captured image  10  input from the imaging apparatus  2  is genuine, that is, whether the coin  100  is genuine, and then, outputs the classification result. The classification result is input to a controller that is provided in the vending machine and manages the actions of the vending machine. The controller performs various actions on the basis of the input classification result. Hereinafter, the processing of classifying whether a coin  100  appearing in a captured image  10  is genuine by the classification apparatus  3  is referred to as “coin classification processing.” 
     In this embodiment, the classification apparatus  3  is one type of computer apparatus and includes a central processing unit (CPU)  300  and a storage  310 . The storage  310  is formed of a non-transitory recording medium readable by the CPU  300 , such as a read only memory (ROM) and a random access memory (RAM). The storage  310  stores a control program  311  for controlling the classification apparatus  3  (computer apparatus). The CPU  300  executes the control program  311  in the storage  310 , so that various functional blocks are formed in the classification apparatus  3 . 
     The various functions of the classification apparatus  3  may be partially or entirely achieved by a dedicated hardware circuit that requires no program (software) for executing the functions and includes a logic circuit. The storage  310  may include a non-transitory computer-readable recording medium other than the ROM and the RAM. The storage  310  may include, for example, a small hard disk drive and a solid state drive (SSD). 
       FIG. 3  illustrates a plurality of functional blocks of the classification apparatus  3 . As illustrated in  FIG. 3 , the classification apparatus  3  includes a conversion unit  30 , a feature image generation unit  31 , and a classification unit  32  as the functional blocks. The conversion unit  30  converts a captured image  10  input from the imaging apparatus  2  from a color image to a gray scale image, and outputs the captured image  10  after the conversion as a captured image  11 . 
     The feature image generation unit  31  generates, on the basis of the captured image  11  in which the coin  100  appears, a feature image  20  showing the feature of the coin  100 . The classification unit  32  classifies whether a coin  100  appearing in a captured image  11  is genuine on the basis of the feature image  20  generated by the feature image generation unit  31 , and outputs a classification result  21 . In this embodiment, the classification unit  32  compares the feature image  20  generated from the captured image  11  by the feature image generation unit  31  with a template feature image  22  showing the feature of the authentic coin  100  to classify, on the basis of the comparison result, whether the coin  100  appearing in the captured image  11  is genuine. The feature image generation unit  31  may be referred to as a “feature image generation device  31 .” 
       FIG. 4  illustrates a configuration of the feature image generation unit  31 . As illustrated in  FIG. 4 , the feature image generation unit  31  includes a first image generation unit  40  and a second image generation unit  50 . The first image generation unit  40  generates, on the basis of the captured image  11 , a first image  24  showing a coin  100 . The second image generation unit  50  generates, as a feature image  20 , at least a part of a rotational composite image obtained by composition of a plurality of rotated images obtained  1   w  rotating the first image  24  generated by the first image generation unit  40 . 
     The first image generation unit  40  includes an extraction unit  41  and an edge image generation unit  42 . The extraction unit  41  extracts, from the captured image  11 , a coin region  23  in which the coin  100  appears. The edge image generation unit  42  performs edge detection on the coin region  23  extracted by the extraction unit  41 , thereby generating an edge image as the first image  24 . 
     &lt;Flow of Coin Classification Processing&gt; 
     The following will describe a series of actions of the classification apparatus  3  when the classification apparatus  3  performs the coin classification processing while a vending machine is in operation.  FIG. 5  is a flowchart illustrating the coin classification processing. 
     As illustrated in  FIG. 5 , in Step s 1 , when receiving a captured image  10  from the imaging apparatus  2 , the classification apparatus  3  converts the captured image  10  from a color image to a gray scale image by the conversion unit  30 , and sets a resultant captured image  11  as a process target. Hereinafter, a captured image  11  to be processed may be referred to as a “processing target image  11 .” 
     Then, in Step s 2 , the extraction unit  41  extracts a coin region  23  in which a coin  100  appears from a processing target image  11 . In Step s 3 , then, the edge image generation unit  42  performs edge detection on the coin region  23  extracted by the extraction unit  41  to generate an edge image as a first image  24  showing the coin  100 . In Step s 4 , then, the second image generation unit  50  generates, as a feature image  20 , at least a part of a rotational composite image obtained by composition of a plurality of rotated images obtained by rotating the edge image (first image  24 ) generated by the edge image generation unit  42 . 
     In Step s 5 , then, the classification unit  32  compares the feature image  20  generated by the second image generation unit  50  with the template feature image  22 , and on the basis of the comparison result, classifies whether the coin  100  appearing in the processing target image  11  is genuine. In other words, the classification unit  32  classifies whether the coin  100  appearing in the captured image  10  generated by the imaging apparatus  2  is genuine on the basis of the result of the comparison between the feature image  20  and the template feature image  22 . In Step s 6 , then, the classification unit  32  outputs a classification result  21  of Step s 5  to a controller provided in the vending machine. If the coin  100  appearing in the captured image  10  is not genuine, in other words, if the coin  100  appearing in the captured image  10  does not correspond to genuine one, the controller issues an alarm to the outside by, for example, sounding an alarm through a loudspeaker or displaying alarming information on the display. 
     Then, when receiving a new captured image  10 , the classification apparatus  3  executes Steps s 2  to s 6  on a captured image  11  obtained from the captured image  10  as a new process target. Hereinafter, the classification apparatus  3  performs similar actions every time it receives a captured image  10 . 
     &lt;Detailed Description of Elements&gt; 
     The following will describe the actions of the extraction unit  41 , the edge image generation unit  42 , the second image generation unit  50 , and the classification unit  32  in more detail. 
     &lt;Extraction Unit&gt; 
       FIG. 6  schematically illustrates an example of a coin region  23  extracted from a captured image  11  by the extraction unit  41 . The coin region  23  can be extracted from the captured image  11  by various methods. 
     One example is a first extraction method using a fact that a coin  100  has a circular outline. In the first extraction method, first, edge detection is performed on a captured image  11  to generate an edge image. As the method of generating an edge image, for example, the Sobel method, the Laplacian method, the Canny method, and any other method can be used. Next, a circular region is extracted from the generated edge image. For example, the Hough transform is used as the method of extracting a circular region. Then, the circular region in the captured image  11 , located at the same position as the position of the circular region in an edge image, is set as a coin region  23 . 
     Another method is a second extraction method of extracting a coin region  23  from a captured image  11  using background subtraction and labeling. In the second extraction method, first, a background subtraction image showing a difference between the captured image  11  and a background image (image in which only the captured image  11  appears) is generated, and the generated background subtraction image is binarized. Then, labeling such as 4-connection is performed on a binary background subtraction image. Then, a partial region in the captured image  11 , which is located at the same position as the position of the connection region (independent region) obtained as a result of the labeling, in the binary background subtraction image is set as the coin region  23 . 
     In this embodiment, the extraction unit  41  extracts a coin region  23  from a captured image  11  by a method different from the two methods described above. The following will describe the action of the extraction unit  41  according to this embodiment. The extraction unit  41  may extract a coin region  23  from a captured image  11  by any one of the two methods described above. 
     First, the extraction unit  41  generates a background subtraction image showing a difference between a captured image  11  and a background image  60  (an image in which only the background of the captured image  11  appears) and binarizes the generated background subtraction image.  FIG. 7  schematically illustrates a background image  60 , and  FIG. 8  schematically illustrates a binary background subtraction image  61 . In the binary images schematically illustrated in  FIG. 8  and the following drawings, a region (high-intensity region) having a pixel value “1” is shown in black, and a region (low-intensity region) having a pixel value “0” is shown in white. The background image  60  is stored in advance in the storage  310  of the classification apparatus  3 . 
     Next, the extraction unit  41  performs, on the binary background subtraction image  61 , template matching using a binary outline template  62  indicating the outline of the coin  100 . Specifically, the extraction unit  41  identifies where in the background subtraction image  61  a region similar to the outline template  62  is located. In other words, the extraction unit  41  identifies where in the background subtraction image  61  a region corresponding to the outline of the coin  100  indicated in the outline template  62  is located.  FIG. 9  schematically illustrates the outline template  62 . The outline template  62  is stored in advance in the storage  310  of the classification apparatus  3 . 
     In template matching, as illustrated in  FIG. 10 , the extraction unit  41  causes the outline template  62  to move incrementally in a raster scanning direction on the background subtraction image  61 . In other words, the extraction unit  41  subjects the outline template  62  to raster scanning on the background subtraction image  61 . In this scanning, the extraction unit  41  generates, at each position of the outline template  62 , an AND image of the outline template  62  and a partial region of the background subtraction image  61  overlapping the outline template  62 . This results in the generation of a plurality of binary AND images. Then, the extraction unit  41  identifies, on the background subtraction image  61 , the position of the outline template  62  used in the generation of an AND image having the largest number of pixels (high-intensity pixels) having a pixel value “l” from among the plurality of generated AND images. This position is a position at which a region similar to that of the outline template  62  is located in the background subtraction image  61 . Then, as illustrated in  FIG. 11 , the extraction unit  41  extracts, as the coin region  23 , a partial region  11   a  in the captured image  11  located at the same position as the identified position. In other words, the extraction unit  41  extracts, as the coin region  23 , a partial region  11   a  in the captured image  11  that overlaps the outline template  62  when the outline template  62  is placed at the same position as the identified position in the captured image  11 . In this extraction, in the partial region  11   a , the coin region  23  may be a region having a pixel value “0” of each pixel outside the circle indicated by the outline template  62  on the partial region  11   a . The coin region  23  extracted by the extraction unit  41  is a gray scale image. The outline of the coin region  23  is quadrangular in this embodiment, which may be circular. 
     &lt;Edge Image Generation Unit&gt; 
     The edge image generation unit  42  performs edge detection on a coin region  23  extracted by the extraction unit  41  to generate an edge image  65  using the Sobel method, the Laplacian method, the Canny method, or any other method. In this embodiment, the edge image generation unit  42  uses, for example, the Sobel method that is easy to process. The edge image  65  is a binary image.  FIG. 12  schematically illustrates the edge image  65 . 
     &lt;Second Image Generation Unit&gt; 
     The second image generation unit  50  generates, as a feature image  20 , at least a part of a rotational composite image obtained by composition of a plurality of rotated images obtained by rotating the edge image  65  generated by the edge image generation unit  42 . In this embodiment, the second image generation unit  50  generates, as a feature image  20 , a rotational composite image obtained by composition of a plurality of rotated images obtained by rotating an edge image  65 . Herein, the rotation of the edge image  65  may be the rotation with a rotation angle of 0°. The following will describe a method of generating the rotational composite image. 
       FIG. 13  is a diagram for explaining the method of generating a rotational composite image  70  (second image  25 ) by the second image generation unit  50 . The second image generation unit  50  causes an edge image  65  to rotate in increments of a predetermined angle α, thereby generating a plurality of rotated images  65   a  as illustrated in  FIG. 13 . Herein, the second image generation unit  50  causes the edge image  65  to rotate in increments of the predetermined angle α until a total of rotation angles is equal to (360°−α). In this embodiment, the second image generation unit  50  causes an edge image  65  to rotate in increments of, for example, 2° (α=2°), thereby generating  180  rotated images  65   a . Then, the second image generation unit  50  composites the plurality of rotated images  65   a  generated to generate a rotational composite image  70 . Specifically, the second image generation unit  50  averages a plurality of rotated images  65   a  with the centers thereof coinciding with each other, thereby setting a resultant averaged image as a rotational composite image  70 . The second image generation unit  50  uses the generated rotational composite image  70  as a feature image  20  showing the feature of the coin  100  appearing in the captured image  11 . 
     In the composition of a plurality of rotated images  65   a , the second image generation unit  50  does not use a region of each rotated image  65   a  that extends beyond the outline of a rotated image  65   a  (that is, an edge image  65  that has not rotated) having a rotation angle of 0°. The rotational composite image  70  is accordingly a gray scale image equal in size to the edge image  65 . 
     As described above, the imaging apparatus  2  captures an image of a rotating coin  100 , thereby generating a captured image  10  in which the coin  100  appears. Thus, the rotation angle (rotation angle of a pattern provided to the coin  100 ) of the coin  100  appearing in the captured image  11 , generated by the conversion unit  30  of the classification apparatus  3 , is not always the same. That is to say, the circumferential orientation of the coin  100  appearing in the captured image  11  is not always constant. Meanwhile, the rotational composite image  70  is obtained by composition of a plurality of rotated images  65   a  obtained by rotating an edge image  65  showing the coin  100  appearing in the captured image  11 , and thus, even when the rotation angle of the coin  100  appearing in the captured image  11  varies (even when the circumferential orientation of the coin  100  appearing in the captured image  11  is not constant), for a genuine coin  100 , a rotational composite image  70  obtained from the captured image  11  changes little. It can therefore be said that the rotational composite image  70  is a feature image  20  that is unsusceptible to the rotation angle of the coin  100  appearing in the captured image  11  and shows the feature of the coin  100 . That is, it can be said that the rotational composite image  70  is a feature image  20  that is unsusceptible to the circumferential orientation of the coin  100  appearing in the captured image  11  and shows the feature of the coin  100 . 
     &lt;Classification Unit&gt; 
     The classification unit  32  compares the rotational composite image  70  (feature image  20 ) generated by the second image generation unit  50  with the template feature image  22  showing the feature of the authentic coin  100  to classify, on the basis of the comparison result, whether the coin  100  appearing in the captured image  11  is genuine. A feature image  20  (rotational composite image  70 ) showing the feature of the authentic coin  100 , which is generated by the feature image generation unit  31  from the captured image  11  appearing in the authentic coin  100 , is used as the template feature image  22 . While a vending machine is not in operation, an authentic coin  100  is inserted into the vending machine to obtain a template feature image  22 . The image processing system  1  in the vending machine generates a captured image  11  in which the inserted authentic coin  100  appears. This captured image  11  is referred to as a “standard image  11 .” In the image processing system  1 , the feature image generation unit  31  generates a feature image  20  (rotational composite image  70 ) showing the feature of the authentic coin  100  appearing in the standard image  11  on the basis of the standard image  11 . This feature image  20  is referred to as a “standard feature image  20 .” In this embodiment, the standard feature image  20  serves as a template feature image  22 . The template feature image  22  generated while the vending machine is not in operation is stored in the storage  310  of the classification apparatus  3 . Hereinafter, a feature image  20  (a feature image  20  generated from a captured image  11  in which a coin  100  whose authenticity is classified appears) generated while the vending machine is in operation may be referred to as a “target feature image  20 ” for differentiation from the standard feature image  20 . 
     The classification unit  32  obtains, for example, a degree of similarity (a degree of difference) between the rotational composite image  70  (target feature image  20 ) and the template feature image  22 , thereby comparing these images. In this embodiment, the classification unit  32  uses a sum of absolute difference (SAD) as a value indicating a degree of similarity. A large SAD means a low degree of similarity, while a small SAD means a high degree of similarity. Any other value, for example, a sum of squared difference (SSD) or a normalized correlation coefficient (NCC) may be used as the value indicating a degree of similarity. 
     The classification unit  32  classifies that the coin  100  appearing in the captured image  11  is genuine when the degree of similarity between the rotational composite image  70  and the template feature image  22  is high or classifies that the coin  100  appearing in the captured image  11  is not genuine when the degree of similarity is low. Specifically, the classification unit  32  classifies that the coin  100  appearing in the captured image  11  is genuine when the SAD between the rotational composite image  70  and the template feature image  22  is equal to or smaller than a threshold or classifies that the coin  100  appearing in the captured image  11  is not genuine when the SAD is greater than the threshold. The classification unit  32  then outputs a classification result  21 . 
     The threshold used by the classification unit  32  is decided, for example, after the use of a plurality of authentic coins  100 . Specifically, a plurality of authentic coins  100  are sequentially inserted into a vending machine while the vending machine is not in operation. Then, the image processing system  1  in the vending machine generates a plurality of captured images  11  in which the plurality of authentic coins  100  inserted into the vending machine individually appear. For each of the plurality of captured images  11  generated, the classification unit  32  obtains a SAD between the rotational composite image  70  generated by the feature image generation unit  31  and the template feature image  22  from the captured image  11 . The classification apparatus  3  then decides the maximum value of a plurality of SADs obtained by the classification unit  32  as a threshold. The decided threshold is stored in the storage  310  of the classification apparatus  3 . 
     A threshold to be used by the classification unit  32  is decided in this manner, and thus, when the type of the authentic coin  100  is changed, an authentic coin  100  after the change is inserted into the vending machine that is not in operation, so that a threshold corresponding to the authentic coin  100  after the change is decided. Even when the type of an authentic coin  100  is changed, accordingly, whether a coin  100  inserted into a vending machine is genuine, that is, whether it is an authentic coin  100  can be classified properly. 
     Although a binary edge image  65  is used as a first image  24  showing a coin  100  appearing in a captured image  11  in the example above, a coin region  23  of a gray scale image may be used as the first image  24 . In this case, a rotational composite image obtained by composition of a plurality of rotated images obtained by rotating a coin region  23  is set as a feature image  20 . 
     As described above, the feature image generation apparatus  31  according to this embodiment generates, as a feature image  20 , at least a part of a rotational composite image  70  obtained by composition of a plurality of rotated images obtained by rotating a first image  24  showing a coin  100 . The feature image  20  is unsusceptible to the rotation angle of a coin  100  appearing in a captured image  11  (unsusceptible to the circumferential orientation of the coin  100 ). Thus, even when the rotation angle (rotation attitude) of a coin  100  appearing in a captured image  11  varies, the feature images  20  (target feature image  20 , standard feature image  20 ) generated by the feature image generation apparatus  31  can be used to classify more precisely whether a coin  100  appearing in the captured image  11  is a genuine one. In other words, even when the circumferential orientation of a coin  100  appearing in a captured image  11  is not constant, whether a coin  100  appearing in the captured image  11  is genuine can be classified more precisely using the feature image  20  generated by the feature image generation apparatus  31 . This improves accuracy of classification. 
     A binary edge image  65  is set as a first image  24  in this embodiment, and accordingly, the effect of a change in the brightness of a captured region in the imaging apparatus  2  on the first image  24  can be more restricted than when a coin region  23  of a gray scale image is set as the first image  24 . This restricts the effect of a change in the brightness of a captured region in which an image of a coin  100  is captured on the feature image  20 , improving the accuracy of classifying the authenticity of a coin  100 . 
     The extraction unit  41  extracts a coin region  23  from a captured image  11  using template matching in this embodiment, and accordingly, the extraction processing can be more simplified than when the first extraction method involving the Hough transform or the second extraction method involving labeling is used. 
     In this embodiment, the classification unit  32  compares the feature image  20  generated from the captured image  11  with the template feature image  22  using a SAD or the like to classify, on the basis of the comparison result, whether the coin  100  appearing in the captured image  11  is genuine, leading to simplified classification processing. 
     &lt;Various Modifications&gt; 
     The following will describe various modifications. 
     &lt;First Modification&gt; 
     The standard feature image  20  is employed as a template feature image  22  to be compared with a target feature image  20  in the example above. Alternatively, a composite image, obtained by composition of a plurality of standard feature images  20  individually generated from a plurality of standard images  11  in which a plurality of different authentic coins  100  individually appear, may be set as a template feature image  22 . 
       FIG. 14  is a diagram for explaining a method of generating a template feature image  22  according to this modification. In the example of  FIG. 14 , in the generation of a template feature image  22 , four standard feature images  20  are used that are generated individually from captured images  11  in which four different authentic coins  100  individually appear. 
     In this modification, when the image processing system  1  generates a template feature image  22 , a plurality of authentic coins  100  are sequentially inserted into a vending machine while the vending machine is not in operation. Then, the image processing system  1  in the vending machine generates a plurality of standard images  11  in which the plurality of authentic coins  100  inserted into the vending machine individually appear. For each of the plurality of generated standard images  11 , the feature image generation unit  31  generates a standard feature image  20  from the standard image  11 . The feature image generation unit  31  then composites the plurality of generated standard feature images  20 , and sets a resultant composite image  80  (see  FIG. 14 ) as a template feature image  22 . For example, the feature image generation unit  31  averages a plurality of standard feature images  20  and sets a resultant averaged image (composite image  80 ) as a template feature image  22 . 
     In this way, a composite image  80  obtained by composition of a plurality of standard feature images  20  individually showing the features of different authentic coins  100  is set as a template feature image  22 , and accordingly, the effect of an individual difference of the authentic coin  100  on the coin classification processing can be more restricted than when a standard feature image  20  is set as a template feature image  22  without any change. Even when the distance between a coin  100  and an imaging apparatus  2  varies in image capturing of the coin  100  by the imaging apparatus  2 , the effect of the variations on the coin classification processing can be restricted. This improves the accuracy of the coin classification processing. 
     Unlike this embodiment, when a first image  24  showing an authentic coin  100  is used as a standard feature image  20  without any change, in the composition of a plurality of standard feature images  20 , the orientations of the plurality of standard feature images  20  need to be aligned such that the rotation angles (rotation attitudes) of a plurality of authentic coins  100  individually appearing in the plurality of standard feature images  20  coincide with each other. 
     In this modification, contrastingly, a rotational composite image  70  obtained by composition of a plurality of rotated images obtained by rotating a first image  24  showing an authentic coin  100  is used as a standard feature image  20 , thereby eliminating the need for aligning the orientations of a plurality of standard feature images  20  in the composition of the plurality of standard feature images  20 . The processing of generating a template feature image  22  can therefore be simplified. 
     When the authentic coin  100  to be used in the generation of a template feature image  22  has a large scratch, even if a plurality of standard feature images  20  are composited to generate a template feature image  22 , the effect of the scratch appears in the template feature image  22 , which may reduce the accuracy of the coin classification processing. 
     Therefore, if pixel values of a plurality of pixels at the same position, which are individually included in a plurality of standard feature images  20  individually showing the features of a plurality of authentic coins  100 , include a pixel value considerably different from the other pixel values, a template feature image  22  may be generated without using a standard feature image  20  including the pixel having the considerably different pixel value. This restricts the use of a standard feature image  20  showing the feature of an authentic coin having a large scratch in the generation of a template feature image  22 . This restricts a decrease in the accuracy of the coin classification processing. 
     &lt;Second Modification&gt; 
     Although the second image generation unit  50  sets, as a feature image  20 , a rotational composite image  70  obtained by composition of a plurality of rotated images obtained by rotating one image  24  showing a coin  100  in the example above, the feature image  20  may be a part of the rotational composite image  70 . Hereinafter, a part of the rotational composite image  70  serving as the feature image  20  is referred to as a “partial feature region  75 .” 
       FIG. 15  illustrates an example of a partial feature region  75  (feature image  20 ). In the example shown in  FIG. 15 , in a quadrangle rotational composite image  70 , a rectangular partial region extending from a center  71  to an upper side  72  is set as the partial feature region  75 . The partial feature region  75  illustrated in  FIG. 15  includes a plurality of pixels in each of the row direction and the column direction. The partial feature region  75  may have a shape other than a rectangle. 
     For example, the second image generation unit  50  generates a rotational composite image  70  on the basis of a first image  24  showing a coin  100 , and then, extracts a partial feature region  75  from the rotational composite image  70 , thereby generating a feature image  20 . 
     The second image generation unit  50  can generate a feature image  20  without generating a rotational composite image  70 .  FIG. 16  is a diagram for explaining the method therefor. As illustrated in  FIG. 16 , the second image generation unit  50  sets an extraction window  90  with respect to the first image  24  generated by the first image generation unit  40  and extracts a partial region  241  in the extraction window  90 . The extraction window  90  is equal in size to the partial feature region  75 . The extraction window  90  is set, in the first image  24 , at the same position as the position of the partial feature region  75  in the rotational composite image  70  (see  FIG. 15 ). The thus extracted partial region  241  is referred to as a “reference partial region  241 .” 
     Next, the second image generation unit  50  causes the extraction window  90  to rotate about a center  240  of the first image  24  in increments of a predetermined angle θ, thereby extracting a partial region  241  in the extraction window  90  at each rotation angle. Herein, the second image generation unit  50  causes the extraction window  90  to rotate about the center  240  of the first image  24  in increments of the predetermined angle β until a total of rotation angles reaches (360°−β), where β is, for example, 2°. The second image generation unit  50  then composites the plurality of obtained partial regions  241 , thereby generating a composite image. In this generation, in the plurality of obtained partial regions  241 , the second image generation unit  50  causes the partial regions  241  except for the reference partial region  241  to rotate so as to align with the outline of the reference partial region  241 , and then, composites the plurality of partial regions  241 . For example, the second image generation unit  50  averages the plurality of obtained partial regions  241  to generate an averaged image, thereby generating a composite image of the plurality of partial regions  241 . The averaged image (composite image) serves as a partial feature region  75 . 
     Setting a part of a rotational composite image  70  as a feature image  20  as described above reduces the number of pixels of a feature image  20 . This simplifies the processing by the classification unit  32  using a feature image  20 . 
     When the second image generation unit  50  causes an extraction window  90  to rotate in increments of a predetermined angle, extracts a partial region  241  in the extraction window  90  at each rotation angle, and composites the plurality of obtained partial regions  241  to generate a feature image  20 , the number of pixels of images handled in the generation of the feature image  20  can be reduced, leading to simplified processing of generating a feature image  20 . 
     Which part of the rotational composite image  70  is set as a partial feature region  75  is decided on the basis of, for example, the position of a pattern shown in the authentic coin  100 . That is, a part of the rotational composite image  70  is set in the partial feature region  75  such that the feature of the coin  100  is sufficiently exhibited in the partial feature region  75 . 
     For example, when the pattern of the authentic coin  100  is present almost entirely on the main surface  100   a  of the coin  100  as illustrated in  FIG. 2 , as in this example, a partial feature region  75  is desirably set so as to extend from the center  71  to the side  72  in the rotational composite image  70 . 
     When the pattern of the coin  100  is present only on the peripheral edge portion of the main surface  100   a  of the coin  100 , as illustrated in  FIG. 17 , only the edge portion of the rotational composite image  70  may be set as the partial feature region  75 . 
     When the pattern of the coin  100  is present almost entirely on the main surface  100   a  of the coin  100 , a partial feature region  75  may be linear as illustrated in  FIG. 18 . The partial feature region  75  illustrated in  FIG. 18  includes a plurality of pixels in the column direction and only one pixel in the row direction. 
     &lt;Other Modifications&gt; 
     Although the image processing system  1  is introduced into a vending machine in the example above, it may be introduced into any other apparatus or in any other place. 
     For example, the image processing system  1  may be introduced into a production line of assembling a product in a factory. More specifically, the image processing system  1  may be introduced into, for example, a production line of mounting a plurality of components on a substrate. In this case, the imaging apparatus  2  captures an image of the substrate including a plurality of components mounted thereon. The feature image generation unit  31  generates, on the basis of the captured image  11  in which a substrate including a plurality of components mounted thereon appears, a feature image showing the feature of the substrate. The classification unit  32  classifies, on the basis of the feature image generated by the feature image generation unit  31 , whether the substrate including a plurality of components mounted thereon, which appears in the captured image  11 , corresponds to a substrate including a plurality of components properly mounted thereon, that is, whether a plurality of components are properly mounted on a substrate. A plurality of components on a substrate can be treated similarly to the pattern on the surface of a coin, and thus, as in the manner described above, the image processing system  1  can classify whether a plurality of components are properly mounted on a substrate. This enables the detection of, for example, erroneous mounting of a component and an omission of a to-be-mounted component. 
     The image processing system  1  may be introduced into a production line of producing snacks in a factory. More specifically, the image processing system  1  may be introduced into a production line of producing shacks having a pattern on their surfaces, such as cookies and chocolates. In this case, an image of the produced snack is captured by the imaging apparatus  2 . The feature image generation unit  31  generates a feature image showing the feature of the snack on the basis of the captured image  11  in which the snack appears. The classification unit  32  classifies, on the basis of the feature image generated by the feature image generation unit  31 , whether the snack appearing in the captured image  11  corresponds to a properly produced snack (non-defective snack), that is, whether the snack appearing in the captured image  11  has been properly produced. The pattern on the surface of the snack can be handled similarly to the pattern of the surface of a coin, and accordingly, the image processing system  1  can classify whether the snack has been properly produced as in the manner described above. This enables the detection of, for example, a distortion and an omission of the pattern of a snack. 
     The image processing system  1  may be introduced into a conveyor-belt sushi restaurant. In the conveyor-belt sushi restaurant, a price corresponding to the pattern of a dish on which a product is put may be set as the price of a product such as sushi. Besides, in the conveyor-belt sushi restaurant, a total of the prices corresponding to the patterns of the dishes picked up by a customer may be calculated as a price of food and drink. The image processing system  1  introduced into such a conveyor-belt sushi restaurant identifies the pattern of the dish picked up by a customer through image processing. 
     Specifically, when a guest picks up a dish from the revolving conveyor belt with a plurality of dishes on which sushi or the like is put, the imaging apparatus  2  captures an image of the dish. The feature image generation unit  31  generates a feature image showing the feature of the dish on the basis of the captured image  11  in which the dish appears. 
     Herein, a pattern is provided to an edge portion of a dish, and a product such as sushi is put at the central portion (portion with no pattern) of the dish. The central portion of an edge image  65  generated by the first image generation unit  40  of the feature image generation unit  31  shows a product and does not show the feature of the dish, and thus, the first image generation unit  40  sets the portion of the edge image  65  except for the central portion as the first image  24  showing a dish. The second image generation unit  50  of the feature image generation unit  31  sets, as a feature image, at least a part of a rotational composite image obtained by composition of a plurality of rotated images obtained by rotating the first image  24 . 
     The classification unit  32  classifies whether the dish appearing in the captured image  11  corresponds to a dish having a predetermined pattern on the basis of the feature image generated by the feature image generation unit  31 . That is to say, the classification unit  32  classifies whether the pattern of the dish appearing in the captured image  11  corresponds to a predetermined pattern on the basis of a feature amount. The pattern on the front surface of the dish can be handled similarly to the pattern on the surface of the coin, and accordingly, as in the manner described above, the image processing system  1  can classify whether the dish appearing in the captured image  11  corresponds to the dish having a predetermined pattern. For each of a plurality of types of patterns individually provided to a plurality of types of dishes used in a conveyor-belt sushi restaurant, the classification unit  34  classifies whether the pattern corresponds to the pattern of the dish appearing in the captured image  11 . Thus, a price corresponding to the pattern of the dish appearing in the captured image  11  can be identified automatically. This enables automatic calculation of a total of the prices corresponding to the patterns of the dishes picked up by a guest, that is, the price of food and drink. 
     The image processing system  1  may be introduced into a distribution depot. In the distribution depot, a sticker labelled with a delivery address or the like may be attached to a package such as a cardboard box. The image processing system  1  classifies whether the sticker attached to the package corresponds to a proper sticker, that is, whether a proper sticker is attached to the package. In this case, the imaging apparatus  2  captures an image of the sticker attached to the package. The feature image generation unit  31  generates a feature image showing the feature of the sticker on the basis of a captured image  11  in which the sticker appears. The classification unit  32  classifies, on the basis of the feature image generated by the feature image generation unit  31 , whether the sticker appearing in the captured image  11  corresponds to a proper sticker, that is, whether the sticker appearing in the captured image  11  is proper one. The characters or the like on the sticker surface can be handled similarly to the pattern on the coin surface, and thus, as in the manner described above, the image processing system  1  can classify whether a proper sticker is attached to the package. This enables the detection of, for example, erroneous attachment of a sticker.