Patent Publication Number: US-10311559-B2

Title: Information processing apparatus, information processing method, and storage medium

Description:
TECHNICAL FIELD 
     The present invention relates to a method of picking up an image of an object and determining whether the object is normal or abnormal on the basis of the picked-up image. 
     BACKGROUND ART 
     A device has been proposed in which, when a signal such as an image is input, and an attribute or the like of the input signal is discriminated by a discriminator, a reason of a discrimination result is visualized and presented to a user. For example, in the field of an automatic appearance inspection, when a determination is made as to whether an inspection target is normal or abnormal by using an image including an inspection target, if a result of the determination that the inspection target is abnormal and also the a reason for the above-described determination can be provided, this information is useful to the user. That is, when an area corresponding to a cause of the anomaly can be visualized as an image, the user can intuitively find out a determination reference of an automatic appearance inspection apparatus. Accordingly, the above-described configuration is useful when a parameter related to the inspection is adjusted and when the number of occurrences of the particular abnormal patterns is found out to provide a feedback to a process in a production line as a countermeasure to carry out a modification. 
     For example, PTL 1 discloses that one determination image is created from a plurality of images obtained by shooting the inspection target object under a plurality of illumination conditions, and the normal/abnormal inspection is performed by using the determination image. With regard to the determination image created by the method according to PTL 1, in a case where the inspection target object is determined as abnormal, an area where an anomaly exists is distinguished from the other normal area to be visualized, and a user can easily understand which area is abnormal. 
     However, the creation of the determination image according to PTL 1 is optimized in accordance with previously set desired inspection items, and it is difficult to cope with a complicated inspection or an unidentified defect. 
     In view of the above, PTL 2 discloses a method of extracting a previously selected feature amount from an input image and determining whether it is normal or abnormal by using the feature amount without generating the determination image as in PTL 1. According to this method, by previously learning a feature amount that affects the normal or abnormal discrimination without generating the determination image, it is possible to accurately determine whether it is normal or abnormal. 
     However, even when the method of outputting the inspection result without creating the determination image as in PTL 2 is employed, an analysis on the result may also be desired to be carried out in some cases. That is, although a normal/abnormal discrimination accuracy is extremely important in the appearance inspection of parts, finding out an anomaly cause corresponding to additional information (a type of the anomaly, a location, or the like) and easily grasping a tendency may also be useful information to the user in many cases. According to the method disclosed in PTL 2, a problem occurs that the anomaly cause is not presented to the user. 
     CITATION LIST 
     Patent Literature 
     
         
         PTL 1: Japanese Patent Laid-Open No. 2010-175305 
         PTL 2: Japanese Patent Laid-Open No. 2010-102690 
       
    
     SUMMARY OF INVENTION 
     To address the above-described problem, a non-defective product inspection apparatus according to an aspect of the present specification includes, for example, an extraction unit configured to extract a plurality of feature amounts from an image including an inspection target object, a determination unit configured to determine an anomaly degree of the inspection target object on the basis of the extracted feature amounts, and an image generation unit configured to generate a defect display image representing a defect included in the inspection target object on the basis of contribution degrees of the respective feature amounts with respect to the anomaly degree determined by the determination unit. 
     In addition, an information processing apparatus according to an aspect of the present specification includes, for example, an extraction unit configured to extract a plurality of feature amounts from a plurality of images including an inspection target object, a determination unit configured to determine an anomaly degree of the inspection target object on the basis of the extracted feature amounts, and an image generation unit configured to generate an image in which a defect included in the inspection target object is emphasized and displayed, by synthesizing the plurality of images to one another on the basis of contribution degrees of the respective feature amounts with respect to the anomaly degree determined by the determination unit. 
     According to the present specification, it is possible to present the determination result with respect to the input data and also the reason of the determination result to the user. 
     Further features of the present invention will become apparent from the following description of embodiments with reference to the attached drawings. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  illustrates a configuration example of an inspection system using an information processing apparatus according to a first embodiment. 
         FIG. 2  illustrates a display example of a display apparatus according to the first embodiment. 
         FIG. 3  is a processing flow chart according to the first embodiment. 
         FIG. 4  is a processing flow chart according to the first embodiment. 
         FIG. 5A  is a functional block diagram of a related-art apparatus. 
         FIG. 5B  is a functional block diagram of the information processing apparatus according to the first embodiment. 
         FIG. 6  is a block diagram illustrating a detail of a processing content of a map integration unit of  FIG. 5B  according to the first embodiment. 
         FIG. 7  is an explanatory diagram for describing a case of determining importance degrees of respective feature amounts in an anomaly score according to the first and second embodiments. 
         FIG. 8  illustrates a configuration example of the inspection system using the information processing apparatus according to the second and third embodiments. 
         FIG. 9  is a functional block diagram of the information processing apparatus according to the second embodiment. 
         FIG. 10  is a block diagram illustrating a detail of the processing content of the map integration unit of  FIG. 9  according to the second embodiment. 
         FIG. 11  is a functional block diagram of the information processing apparatus according to the third embodiment. 
         FIG. 12  is a block diagram illustrating a detail of a processing content of an image synthesis unit of  FIG. 11  according to the third embodiment. 
         FIG. 13  is a functional block diagram of the information processing apparatus according to the fourth embodiment. 
         FIG. 14  is a block diagram illustrating a detail of the processing content of the map integration unit of  FIG. 13  according to the fourth embodiment. 
         FIG. 15  is a processing flow chart according to the third embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, with respect to the drawings, embodiments (exemplary embodiments) of the present invention will be described. 
     Before the respective embodiments of the present invention will be described, a hardware configuration to which an information processing apparatus  101  according to the respective embodiments is mounted will be described with respect to  FIG. 1 . 
     In  FIG. 1 , a CPU  1010  controls the respective devices connected via a bus  1000  in an overall manner. The CPU  1010  reads out process steps and programs stored in a read-only memory (ROM)  1020 . Respective process programs, device drivers, and the like including an operating system (OS) according to the present embodiment are stored in the ROM  1020  and temporarily stored in a random access (RAM)  1030  to be appropriately executed by the CPU  1010 . An input signal is input to an input interface (I/F)  1040  from an external apparatus (such as a display apparatus or an operation apparatus) in such a format that the signal can be processed by the information processing apparatus  101 . An output signal is output from an output I/F  1050  to the external apparatus (display apparatus) in such a format that the signal can be processed by the display apparatus. 
     First Embodiment 
     Hereinafter, a first embodiment of the present invention will be described with respect to the drawings. 
       FIG. 1  is a conceptual diagram of an appearance inspection system using the information processing apparatus  101  according to the present embodiment. 
     An image pickup apparatus  103  is constituted by a video camera or the like that can obtain a picture (image pattern) of a surface of an inspection target object  102  and transmits the obtained video to the information processing apparatus  101 . The information processing apparatus  101  performs information processing for an appearance inspection by using the transmitted video picture. 
     The inspection target object  102  is an object as a target to be determined as a non-defective product or a defective product by the information processing apparatus  101 . Examples of the inspection target object  102  include a rubber formed product used for an industrial product, a metallic part, a glass formed product such as a lens, a plastic formed product, and the like. Irregularities that are not seen on the non-defective product or unevenness or scratches in processing process may be caused on a surface of the inspection target object  102  in some cases. These defects are detected by the appearance inspection, and the object is detected as the defective product. 
     A display apparatus  104  is constituted by a monitor or the like.  FIG. 2  illustrates a display example of a determination result to the display apparatus  104 . Images are displayed on an area  201 . A normal/abnormal classification result corresponding to an image selected on the area  201  is displayed while being emphasized by a bold frame, and OK/NG is displayed on an area  202 . An anomaly score by the discriminator at that time is displayed on an area  203 . An anomaly score map with respect to the selected image is displayed on an area  204 , which represents a situation where a large scratch-like anomaly, which is difficult to be visually recognized with the image alone, can be easily visually recognized on the anomaly score map displayed on the area  204 . That is, while the user checks the image displayed on the area  204 , the user can easily understand why the image selected on the area  201  is determined as abnormal. 
       FIG. 5A  is a functional block of a related-art apparatus that generates a determination image for the visualization from an input image  400  as described in PTL 1.  FIG. 5B  is a functional block of the present embodiment. In  FIG. 5A , a discrimination of the input image  400  is performed in a manner that a determination image generation unit  402  is generated in a determination intermediate image  403 , and a discrimination unit  404  performs a discrimination as to whether the input image  400  is normal or abnormal on the basis of a result of the determination intermediate image  403 . Hereinafter, respective functions constituting the information processing apparatus according to the present embodiment in  FIG. 5B  will be described. 
     An input image  401  is an image of an inspection target object picked up by an image pickup unit. 
     A feature extraction unit  405  extracts feature amounts from the input image  401 . According to the present embodiment, a maximum value, an average value, a variance, a kurtosis, a skewness, a contrast, a maximum gradient, or the like is extracted as feature amounts from an image obtained through a conversion of an input image. Specific processing will be described below. 
     A discrimination unit  406  determines whether the inspection target object included in the input image  401  is normal or abnormal by using the feature amounts extracted by the feature extraction unit  405 . 
     A map generation unit  407  generates maps of the respective feature amounts corresponding to the extracted features from the input image  401 . The maps according to the present embodiment have a two-dimensional array having the same number of dimensions as the input image, and scores are stored in the respective elements. The map is not limited to this format of course. 
     A map integration unit  408  refers to contribution degrees of the respective feature amounts with respect to the discrimination result obtained at the time of the discrimination by the discrimination unit  406  and integrates the plurality of maps generated by the map generation unit  407  for the respective feature amounts to one another to generate a visualized image  409  in which a defect candidate area is visualized. The map integration unit  408  outputs the generated visualized image  409 . 
     The above-described respective functional units are realized while the CPU  1010  extends a program stored in the ROM  1020  onto a RAM  1030  and executes processings following respective flow charts that will be described below. For example, in a case where hardware is constructed as a substitute of the software processing using the CPU  1010 , a computation unit or a circuit corresponding to the processing of each functional unit described herein may be constructed. 
     Hereinafter, the processing flow chart according to the present embodiment will be described with reference to  FIG. 3 . 
     Step S 301   
     In step S 301 , the feature extraction unit  405  extracts N pieces of features from the input image  401  corresponding to the image of the inspection target. Hereinafter, how to determine the feature amounts extracted from the image according to the present embodiment will be described below. 
     First, the input image  401  is decomposed from high frequency components into frequency component images in vertical, horizontal, and diagonal directions of low frequency components by the filter processing based on Haar Wavelet conversion to generate a plurality of hierarchical images. Then, a plurality of feature amounts are extracted from the plurality of generated hierarchical images. The processing based on the Haar Wavelet conversion is performed on the input image  401  by using filters represented by the following four types of matrices (Expressions 1-A to 1-D). 
     
       
         
           
             
               
                 
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     Expression 1-A represents a vertical direction high frequency component filter, Expression 1-B represents a horizontal direction high frequency component filter, Expression 1-C represents a diagonal direction high frequency component filter, and Expression 1-D represents a low frequency component filter. New images are generated which store pixel values corresponding to the images decomposed in terms of the frequency components newly obtained by performing an inner product calculation on 2×2 pixels in the input image  401  by using the filters represented by Expressions 1-A to 1-D. This filter processing is moved over the entire area in the input image  401  without overlapping of the 2×2 areas, and images are obtained in which image sizes of four types of a vertical direction high frequency component image, a horizontal direction high frequency component image, a diagonal direction high frequency component image, and a low frequency component image are halved lengthwise and crosswise. 
     Thereafter, the filter processing is further performed on the low frequency component image similarly as in the input image to obtain four types of images including the vertical direction high frequency component image, the horizontal direction high frequency component image, the diagonal direction high frequency component image, and the low frequency component image in the next hierarchical level. 
     The generation of the images having the image size halved lengthwise and crosswise is repeatedly performed by repeating the above-described processing for decomposing the signal for each frequency and is ended until the repetition is performed up to the hierarchical level at which the decomposition is eventually no longer available. It should be noted that up to how many hierarchical levels the decomposition is performed depends on the size of the input image  401 . For example, in the case of decomposition into 8 hierarchical levels, since four images per hierarchical level are obtained, 8×4=32 types of images are obtained (hierarchical image generation). 
     Next, a plurality of statistical feature amounts are extracted from the 32 types of images. A value such as the maximum value, the average value, the variance, the kurtosis, the skewness, the contrast, or the maximum gradient of the pixel values in each frequency component image (in the hierarchical image) is set as the feature amount extracted herein. Herein, when the number of feature amounts extracted from each of the frequency component images is set as 20, the number of the feature amounts extracted from the single original image is eventually 32×20=640. However, the number of dimensions of the information dealt with in the discrimination processing using all of the feature amounts as many as 640 is high, and a generalizing capability is decreased in many cases by a phenomenon generally called “the curse of dimensionality”. In view of the above, a combination of feature amounts appropriate to the discrimination processing is selected in advance by a mechanical leaning method of searching for a feature amount set with which normality and anomaly are accurately distinguished, and it is possible to realize the highly accurate discrimination processing in the actual inspection. Various methods are proposed as a feature selection method of searching for a satisfactory feature amount combination, and numerous techniques including LASSO, graph cut, a feature selection technique using a genetic algorithm, and the like are exemplified. Hereinafter, the number of feature amounts eventually selected in advance by the feature selection processing is set as N (approximately several tens of feature amounts). It should be noted that any discriminator may be used, and a subspace method is used according to the present embodiment. In addition, according to the present embodiment, the Haar Wavelet conversion is used, but the present invention is not limited to this, and a predetermined conversion with which it is possible to generate a plurality of images from the input image  401  may be used. 
     Step S 302   
     In step S 302 , the discrimination unit  406  obtains the N feature amounts extracted in step S 301  and determines whether an anomaly exists on the basis of a distance from a higher dimension surface representing a normal area that has been learnt as a normal distribution by the subspace method. A anomaly score is defined as a numeric conversion of the distance or the like from the normal distribution by the discriminator. According to the present embodiment, the subspace method is used as the discrimination method by the discrimination unit  406 . When the input entire feature amounts as illustrated in  FIG. 7  is set as X, a hyperplane having an orientation having a maximum pattern distribution variance is learnt by training normal data at the time of the previous learning. A distance D in a direction component orthogonal to the hyperplane from data newly input at the time of testing is used as the distance from the normal distribution, that is, the anomaly score. Thus, an intermediate image for the inspection does not exist, and the normal/abnormal discrimination can be carried out. The distance D can be obtained by subjecting a distribution in a normal feature space desired to be represented by a mean vector Mc (c=1, 2, . . . , C) of the normal data and a hyperplane constituent vector ϕcl (1=1, 2, . . . , L) to an approximate representation by the hyperplane. For the above-described “1”, an appropriate dimension number L may be determined, and the L-dimensional hyperplane may be set in the following description. 
     In the appearance inspection, c denotes the number of classes in a normal state. For example, the inspection in a state in which two normal states are mixed with each other may be executed by setting c=2. For the distance D in the case of c=2, two distances defined by the two normal classes (c=1 and c=2) are calculated, and it is possible to determine as normal when one of the two distances is shorter than or equal to a distance previously set as a threshold. It should be however noted that, since a probability is extremely low that the inspection of two types of parts is performed at the same time by a single inspection process in the appearance inspection in which a precision is demanded in general, c=1 is set hereinafter. In addition, the number of pieces of normal data is set as t. At this time, Mc is represented by Expression 2. 
     
       
         
           
             
               
                 
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     A variance-covariance matrix Σc can be represented by using a feature vector Xci. The variance-covariance matrix Σc is represented by Expression 3. 
     
       
         
           
             
               
                 
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     Furthermore, together with the calculation of the anomaly score D (the distance D) herein, contribution degrees of the N feature amounts with respect to the anomaly score D are output. The contribution degree mentioned herein refers to a rate of the contribution of each of the feature amounts with respect to the anomaly score D and is used by being read in step S 312  when the anomaly score maps are visualized in a later stage. A contribution degree calculation method can be used for the following calculation. When an element in a j-th dimension of a weight W with respect to X′ is set as wj, and an eigenvector is set as e, a relationship with the anomaly score D can be represented by Expression 6. 
     
       
         
           
             
               
                 
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     Accordingly, the contribution degree wj of the j-th dimensional feature amount to be obtained is calculated as Expression 7. 
     
       
         
           
             
               
                 
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     The thus calculated wj is output to be used in the visualization process in step S 306 . In addition, a threshold is previously set with respect to the anomaly score D, and in step S 303 , the discrimination unit determines as normal or abnormal. 
     Step S 304   
     In step S 304 , it is determined as to whether or not defect candidate area visualization based on the anomaly score maps is performed. The user may previously set whether or not the defect candidate area visualization is performed. When it is set that the anomaly score map visualization is not performed, the processing proceeds to step S 305 . When it is set that the anomaly score map visualization is performed, the processing proceeds to step S 306  to perform the defect candidate area visualization. Whether the processing proceeds to step S 306  or step S 305  may be determined on the basis of the normal or abnormal determination result obtained in step S 304 . That is, when it is determined as abnormal, the processing may automatically proceed to step S 306 . 
     Step S 305   
     In step S 305 , the map integration unit  408  outputs information as to whether it is normal or abnormal, the anomaly score, and the like to the display apparatus  104 . 
     Step S 306   
     In step S 306 , the defect area is visualized. This processing will be described by using the processing flow chart of  FIG. 4 . 
     Step S 308   
     In step S 308 , the map generation unit  407  obtains the input image  401 . 
     Step S 309   
     In step S 309 , the map generation unit  407  calculates score maps representing the scores of the feature amounts extracted in step S 301 . For example, when one of the N feature amounts extracted in step S 301  is the maximum value of the hierarchical images obtained by the Haar Wavelet conversion, the map generated by the map generation unit  407  is the same as the hierarchical image for extracting the maximum value in step S 301 . In addition, when one of the N feature amounts extracted in step S 301  is the average value of the hierarchical images obtained by the Haar Wavelet conversion, images obtained by dividing the hierarchical image into grids having an arbitrary size and calculating the average values in the respective grids are set as score maps. 
     Step S 310   
     In step S 310 , the map generation unit  407  performs normalization of the score maps obtained in step S 309 . A plurality of training images including the non-defective inspection target object are previously converted into score maps corresponding to the respective features by a method similar to step S 309 , and an average value and a standard deviation of the obtained score maps are calculated to be held, for example, in the ROM  1020 . Thereafter, the held average value and standard deviation are read out, and the score maps obtained in step S 309  are normalized by using the read values. 
     Step S 311   
     In step S 311 , the map integration unit  408  determines whether only values of the score maps higher than or equal to the threshold are left or all the values are left on the basis of the user setting. If the threshold cutoff is set, all the areas lower than or equal to the threshold are set as 0 in step S 313 . 
     Step S 312   
     In step S 312 , the map integration unit  408  reads the contribution degrees of the feature amounts output in step S 302  and integrates the respective score maps to one another by using the contribution degrees as importance degrees of the respective score maps to create a defect display image. The process of creating the image on which the importance degrees are affected at this time will be illustrated in a conceptual diagram of  FIG. 6 . The conceptual diagram of  FIG. 6  represents a detail of the map integration unit  408  of  FIG. 5B . 
     The importance degree W calculated in Expression 7 holds respective weights w 1  to wN with respect to the input N feature amounts as illustrated in  FIG. 6 , and these weights are transmitted from the discrimination unit  406  to the map integration unit  408 . Then, the respective maps calculated from the N feature amounts are respectively transmitted as Fmap 1  to FmapN from the map generation unit  407 . Thereafter, the respectively corresponding maps are multiplied by the importance degrees, and a linear sum is calculated for each of the same pixels in all the maps to generate a single defect display image. It should be noted that, when the respective score maps are integrated to one another, the respective score maps are once converted to have the same resolution as that of the original input image  401  and then integrated to one another. The defect display image is an image in which the defect area is emphasized and an image indicating a reason why it is determined as abnormal by the discrimination unit  406 . 
     Step S 313   
     In step S 313 , the map integration unit  408  sets all scores of the areas having the scores lower than or equal to the threshold as 0 in the respective maps normalized in step S 310 . Accordingly, since only the defect area has a score, in a case where the defect area is superposed on the input image  401  to be displayed, it is facilitated for the user to recognize the location of the defect area. 
     Step S 307   
     In step S 307 , the map integration unit  408  outputs the score maps generated in step S 306  to the display apparatus  104  to be displayed. In step S 305 , the map integration unit  408  outputs the information as to whether it is normal or abnormal, the anomaly score, and the like, and the processing is ended. 
     According to the present embodiment, the user can intuitively find out the reason why the inspection target is determined as abnormal together with the result of the abnormal determination. For this reason, the above-described configuration is beneficial when the parameter related to the inspection is adjusted and when the number of occurrences of the particular abnormal patterns is found out to provide the feedback to the process in the production line as the countermeasure to carry out the modification. 
     Second Embodiment 
     Hereinafter, a second embodiment (exemplary embodiment) of the present invention will be described with respect to the drawings. According to the second embodiment, in a case where a plurality of images are obtained by shooting a single inspection target and a feature amount is extracted from each of the images to perform the appearance inspection, an image indicating a reason of an inspection result is generated. 
     A hardware configuration to which an information processing apparatus  801  according to the present embodiment is mounted will be described with reference to  FIG. 8 . The configuration of  FIG. 8  is similar to the configuration of  FIG. 1  but is designed such that a large irregularity structure is formed on the inspection target  802 , and a texture anomaly on irregularities is accurately detected. Thus, a difference resides in that eight illumination apparatuses  805  to  812  are provided, and the illumination apparatuses  805  to  812  are controlled by the information processing apparatus  801  as compared with  FIG. 1 . 
       FIG. 8  is a conceptual diagram of the appearance inspection system using the information processing apparatus  801  according to the present embodiment. 
     An image pickup apparatus  803  is constituted by a video camera or the like that can obtain a picture (image pattern) of a surface of the inspection target  802  and transmits the obtained picture to the information processing apparatus  801 . Herein, illumination times of the illumination apparatuses  805  to  812  are controlled in synchronous with the image pickup of the image pickup apparatus  803 , and a plurality of types of images related to the inspection target  802  are shot. In  FIG. 8 , the illumination apparatuses  805  to  812  are exemplified as illuminations having different irradiation angles, but these illumination apparatuses may emit not only visible light or a uniform illumination but also infrared rays or an arbitrary pattern illumination. In particular, in a case where a three-dimensional structure of the inspection target  802  is obtained and used, a plurality of pattern illuminations are emitted to carry out the image pickup. Furthermore, the shooting may be performed under illumination conditions based on combinations of these eight illuminations. According to the present embodiment, descriptions will be given while the image pickup is performed once each under the single illumination condition to obtain eight input images to be processed. 
     The information processing apparatus  801  controls the illumination apparatuses  805  to  812  in the above-described manner and performs the information processing for detecting the anomaly by using the plural types of video pictures which are shot and transmitted by the image pickup apparatus  803 . 
     The inspection target object  802  is an object set as a target to be determined as normal or abnormal by the information processing apparatus  801 . 
     A display apparatus  804  is constituted by a monitor or the like. A determination result display example to the display apparatus  804  is similar to that of the first embodiment as illustrated in  FIG. 2 . 
       FIG. 9  is a functional block diagram of the present embodiment. Hereinafter, respective functions of the information processing apparatus in  FIG. 9  according to the present embodiment will be described. 
     Input images  9005  to  9012  are images in which the inspection target object is shot by the image pickup unit  803  under the illumination environment formed by the illumination apparatuses  805  to  812 . Feature extraction units  9050  to  9057  respectively extract feature amounts from the input images  9005  to  9012 . According to the present embodiment, similarly as in the first embodiment, the maximum value, the average value, the variance, the kurtosis, the skewness, the contrast, the maximum gradient, or the like is extracted from the image obtained by performing the frequency conversion on the input image as the feature amount. 
     A discrimination unit  906  determines whether the inspection target object included in the input images  9005  to  9012  is normal or abnormal by using the feature amounts extracted by the feature extraction units  9050  to  9057 . 
     Map generation units  9070  to  9077  generate respectively corresponding maps of the respective feature amounts corresponding to the extracted features from the input images  9005  to  9012 . 
     A map integration unit  908  refers to contribution degrees of the respective feature amounts with respect to the discrimination result obtained at the time of the discrimination by the discrimination unit  906  and integrates the plurality of maps generated for each of the feature amount by the map generation units  9070  to  9077  to generate a visualized image  909  in which the defect candidate area is visualized. The processing for generating the visualized image  909  is similarly performed as in the first embodiment, but a difference resides in the number of input images. According to the first embodiment, the inspection is performed by using the feature amount group extracted from the single input image, but according to the present embodiment, the inspection is performed by using the feature amount group extracted from the plurality of images similarly like the feature amount group extracted from the single input image according to the first embodiment. Here, a method of generating a visualized image will be described with reference to a concept diagram of  FIG. 10 . The concept diagram of  FIG. 10  represents a detail of the map integration unit  908  of  FIG. 9 . 
     Hereinafter, the processing flow chart according to the present embodiment will be described with reference to  FIG. 3 . 
     (Step S 301 ) 
     The series of processings such as the feature extraction in step S 301  are similar to those of the first embodiment. Feature amounts are respectively extracted from the input images  9005  to  9012  by the respectively corresponding feature extraction units  9050  to  9057 . According to the method for the feature extraction from the single input image, a plurality of hierarchical images are generated through the image decomposition from the high frequency components into images of frequency components in the vertical, horizontal, and diagonal directions of the low frequency components by the filter processing based on the Haar Wavelet conversion. Thereafter, a plurality of feature amounts are extracted from the plurality of generated hierarchical images. With regard to the feature amounts extracted herein too, similarly as in the first embodiment, the feature amount with which the abnormal data can be highly accurately detected is selected in advance. It should be however noted that, according to the present embodiment, since the feature extraction processing before the feature selection is performed on all of the eight input images, the extracted feature amount is selected in a state in which the 8-fold feature amounts are extracted as compared with the first embodiment. Thereafter, a satisfactory combination of the feature amounts is searched for from among all of these extracted features. The selection may be carried out by using LASSO, graph cut, the feature selection technique using the genetic algorithm, and the like in the related art as the feature selection method. As a result of the feature selection, the feature amounts extracted by the feature extraction units  9050  to  9057  do not necessarily need to respectively have the same feature amount set, and also the numbers of extracted feature amounts do not necessarily need to equal to one another. In some cases, the number of features extracted from the image shot under certain illumination conditions may be 0. 
     Hereinafter, with regard to the feature amounts eventually selected by the feature selection processing in advance, the total number of the feature amounts extracted from all the input images is set as several tens of feature amounts. 
     (Step S 302 ) 
     In step S 302 , the feature amounts extracted in step S 301  are transmitted from the feature extraction units  9050  to  9057 , and the normal/abnormal determination is performed by the discrimination unit  906 . It should be noted that, according to the present embodiment, the discrimination unit  906  determines whether an anomaly exists on the basis of a distance from a higher dimension surface representing a normal area that has been learnt as a normal distribution by the subspace method similarly as in the first embodiment. The calculation method for the contribution degrees of the respective feature amounts with respect to the anomaly score is executed similarly as in the first embodiment, and the calculated contribution degrees of the respective feature amounts are output. In step S 303 , the discrimination unit determines whether it is normal or abnormal. 
     (Step S 304 ) 
     In step S 304 , it is determined as to whether or not the defect candidate area visualization based on the anomaly score maps is performed. The user may previously set whether or not the defect candidate area visualization is performed. When it is set that the anomaly score map visualization is not performed, the processing proceeds to step S 305 . When it is set that the anomaly score map visualization is performed, the processing proceeds to step S 306  to perform the defect candidate area visualization. Whether the processing proceeds to step S 306  or step S 305  may be determined on the basis of the determination result as normal or abnormal obtained in step S 304 . That is, when it is determined as abnormal, the processing may automatically proceed to step S 306 . 
     (Step S 305 ) 
     In step S 305 , the map integration unit  908  outputs information as to whether it is normal or abnormal, the anomaly score, and the like to the display apparatus  104 . 
     (Step S 306 ) 
     In step S 306 , the defect area is visualized. This processing will be described by using the processing flow chart of  FIG. 4 . 
     (Step S 308 ) 
     In step S 308 , the map generation units  9070  to  9077  obtain the input images  9005  to  9012 . 
     (Step S 309 ) 
     In step S 309 , the map generation units  9070  to  9077  calculate score maps representing the scores of the feature amounts extracted in step S 301  similarly as in the first embodiment. 
     (Step S 310 ) 
     In step S 310 , the map generation units  9070  to  9077  perform the normalization of the score maps obtained in step S 309  similarly as in the first embodiment. 
     (Step S 311 ) 
     In step S 311 , the map integration unit  908  determines whether only values of the score maps higher than or equal to the threshold are left or all the values are left on the basis of the user setting. If the threshold cutoff is set, all the areas lower than or equal to the threshold are set as 0 in step S 313 . 
     (Step S 312 ) 
     In step S 312 , the map integration unit  908  reads the contribution degrees of the feature amounts output in step S 302  and integrates the respective score maps to one another by using the contribution degrees as the importance degrees of the respective score maps to create the defect display image. The process of creating the image on which the importance degrees are affected at this time will be illustrated in the conceptual diagram of  FIG. 10 . The conceptual diagram of  FIG. 10  represents a detail of the map integration unit  908  of  FIG. 9 . In the map integration at this time too, the plurality of input images are used. Maps Fmap 90701  to FmapN respectively calculated from the feature amounts are multiplied by corresponding importance degrees, and a linear sum is calculated for each of the same pixels in all the maps to generate a single defect display image. It should be noted that, when the respective score maps are integrated to one another, the respective score maps are once converted to have the same resolution as the original input images  9005  to  9012  and then integrated to one another. If the image sizes of the input images  9005  to  9012  are different from each other, those image sizes are converted into the same size (for example, to be matched with the largest size of the input image) and coupled to one another. 
     The defect display image is an image in which the defect area is emphasized and an image indicating a reason why it is determined as abnormal by the discrimination unit  406 . 
     (Step S 313 ) 
     In step S 313 , the map integration unit  908  sets all scores of the areas having the scores lower than or equal to the threshold as 0 in the respective maps normalized in step S 310 . Accordingly, since only the defect area has a score, in a case where the defect area is superposed on the input image  401  to be displayed, it is facilitated for the user to recognize the location of the defect area. 
     (Step S 307 ) 
     In step S 307 , the map integration unit  908  outputs the score maps generated in step S 306  to the display apparatus  104  to be displayed. In step S 305 , the map integration unit  408  outputs the information as to whether it is normal or abnormal, the anomaly score, and the like, and the processing is ended. 
     According to the present embodiment, the user can intuitively find out the reason why the inspection target is determined as abnormal together with the result of the abnormal determination. For this reason, the above-described configuration is beneficial when the parameter related to the inspection is adjusted and when the number of occurrences of the particular abnormal patterns is found out to provide the feedback to the process in the production line as the countermeasure to carry out the modification. In addition to the above, it is possible to find out an important shooting condition depending on the shot images obtained under the plurality of illumination conditions and the contribution degrees of the feature amounts output in S 302  corresponding to the respective images, and a tendency that the defect is more likely to be recognized under which shooting condition. 
     Third Embodiment 
     Hereinafter, a third embodiment (exemplary embodiment) of the present invention will be described with respect to the drawings. Similarly as in the second embodiment, according to a method of the third embodiment, a plurality of images are obtained by shooting the single inspection target. For the image indicating the reason of the inspection result in a case where the feature amount of each of the images is extracted to perform the appearance inspection, a synthesis image in which an anomaly area is emphasized and shot from the input image shot under a plurality of illumination conditions is displayed as the visualization result instead of the visualized image based on the anomaly score maps. According to the present embodiment, for example, in an initial setting, even when the inspection is carried out by performing the shooting without adjusting illumination conditions like the illumination apparatuses  805  to  812  as illustrated in  FIG. 8 , images can be synthesized to one another to obtain a synthesis image as a defect area emphasis image obtained by an optimal combination of the illumination conditions such that it is easy for the user to intuitionally recognize, and the synthesis image can be presented to the user. 
     A difference between the present embodiment and the second embodiment will be described by way of comparison between  FIG. 9  and  FIG. 11 . According to the present embodiment, the map generation units  9070  to  9077  and the map integration unit  908  according to the second embodiment do not exist, and an image synthesis unit  910  that receives an input image and outputs a visualized image  911  exists instead. Thus, the process flow related to S 301  to S 307  illustrated in  FIG. 3  are similar to that of the second embodiment except that the flow for the visualization of the defect area in S 306  is replaced by  FIG. 15  according to the present embodiment. 
     Step S 314   
     In step S 314 , the input images  9005  to  9012  are read to be transmitted to the image synthesis unit  910 . 
     Step S 315   
     The contribution degrees (importance degrees) of the respective feature amounts output in step S 302  are transmitted from the discrimination unit  906  to the image synthesis unit  910 . Thereafter, the importance degrees corresponding to the respective input images are calculated.  FIG. 12  illustrates a detail an example of the processing by the image synthesis unit  910 . The importance degrees corresponding to the respective input images  9005  to  9012  are defined by a sum of the importance degrees corresponding to the respective input images among the respective feature amount importance degrees. A value obtained by integrating the importance degrees calculated with respect to the respective input images may take a decimal format, and takes a two-dimensional matrix formation exceeding a luminance range of 0 to 255. These obtained results are accumulated, and the eventually obtained two-dimensional matrix is normalized again to be contained in the range of 0 to 255 to obtain the visualized image  911 . 
     Fourth Embodiment 
     Hereinafter, a fourth embodiment (exemplary embodiment) of the present invention will be described with respect to the drawings. According to the fourth embodiment, in a case where a plurality of images are obtained by shooting the single inspection target and the appearance inspection is performed by extracting feature amounts from the images, an anomaly degree for each of the images is calculated by a discriminator, and the anomaly degrees by the number of the input images are integrated to one another to determine again whether it is normal or abnormal, and also an image indicating a reason of the inspection result is generated by setting a plurality of anomaly scores at this time as a reference. 
     According to the second embodiment, when the shot images obtained under the plurality of illumination conditions are input, the feature amounts extracted from the picked-up images are integrated to one another to be determined as normal or abnormal by the single discriminator. However, since it takes time to perform the individual shooting in actual use cases in the production line, in particular, if the inspection is not performed until all the shootings are ended, this may be inconvenient in terms of takt time. In view of the above, in a case where the above-described inspection is performed, a line is designed in which inspection apparatuses that detect anomalies having different tendencies for the respective input images are arranged. In the above-described case too, it is aimed at generating an image indicating a reason of an inspection result by using the anomaly score output by each discriminator. 
     In  FIG. 13 , the input images  9005  to  9012  are input, and the feature amounts previously selected in the feature extraction units  9050  to  9057  from the input images  9005  to  9012  are extracted. Anomaly degrees D 130  to D 137  are calculated for the respectively extracted feature amounts in discrimination units  9060  to  9067 . Herein, the discrimination units  9060  to  9067  perform discrimination, for example, by the subspace method or the like, and uses a method with which it is possible to calculate a distance from a distribution of normal data as the anomaly degree. Each discrimination processing and processing of outputting the map by map integration units  9080  to  9087  are similar to those according to the first embodiment where one input image is shot, and the discrimination processing and the visualized image generation are performed. The anomaly degrees by the number of input images are further transmitted to a discrimination unit  90600  to determine a result in a comprehensive manner so that it is determined whether the inspection target is normal or abnormal. The discrimination unit  90600  may use any discrimination method. For example, a simple method may be used such as a method of performing a threshold determination on a total value of the anomaly degrees D 130  to D 137  or a method of previously setting a threshold for each of the anomaly degrees D 130  to D 137  and determining as abnormal in a case where even one of the anomaly degrees exceeds the threshold. Thereafter, feature amount maps Fmap 1300  to Fmap 1307  calculated by the respective map integration units  9080  to  9087  are transmitted to a map integration unit  90800 . Then, the map integration unit  90800  receives the anomaly degrees D 130  to D 137  output by the discrimination units  9060  to  9067 . As illustrated in  FIG. 14 , the corresponding anomaly degrees with respect to the respective feature amount score maps are integrated and accumulated to one another to generate the visualized image  909 . This is based on an idea that, since it is conceivable that the objects having high anomaly degrees strongly reflects the respective anomaly tendencies, this can be treated equivalently with the importance degree according to the first to third embodiments. 
     Other Embodiments 
     Embodiments of the present invention can also be realized by a computer of a system or apparatus that reads out and executes computer executable instructions recorded on a storage medium (e.g., non-transitory computer-readable storage medium) to perform the functions of one or more of the above-described embodiment(s) of the present invention, and by a method performed by the computer of the system or apparatus by, for example, reading out and executing the computer executable instructions from the storage medium to perform the functions of one or more of the above-described embodiment(s). The computer may comprise one or more of a central processing unit (CPU), micro processing unit (MPU), or other circuitry, and may include a network of separate computers or separate computer processors. The computer executable instructions may be provided to the computer, for example, from a network or the storage medium. The storage medium may include, for example, one or more of a hard disk, a random-access memory (RAM), a read only memory (ROM), a storage of distributed computing systems, an optical disk (such as a compact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™), a flash memory device, a memory card, and the like. 
     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. 2014-251881, filed Dec. 12, 2014, and Japanese Patent Application No. 2015-187463, filed Sep. 24, 2015, which are hereby incorporated by reference herein in their entirety.