JP5869987B2 - Article collation apparatus, article collation method, and article collation program - Google Patents

Description

  The present invention relates to an article collation apparatus that collates articles in images obtained by photographing a plurality of articles with previously stored articles, an article collation method using the article collation apparatus, and an article collation program.

  Traditionally, retailers that sell products to end consumers check the actual inventory quantity and understand the difference between the inventory quantity listed in the inventory management table and the actual inventory quantity. For this reason, product verification work is regularly performed. For example, for all products in the store, read the barcode attached to the product, obtain the product number, create the matching inventory table by counting the number for each product based on the acquired product number, and store it in advance Difference information with the actual inventory quantity is obtained by comparing with the managed inventory table.

  However, with the recent diversification of products, there are many cases where a large number of products exist in large stores. For this reason, it is a heavy burden on the retailer to read the bar code of each product and collate the product for inventory management because it requires cost and work time. For this reason, the technique which reduces the collation work of the goods for inventory management is anticipated.

  In order to solve such a problem, Patent Literature 1 recognizes one product from an image obtained by photographing a plurality of products placed on a storage shelf during a product matching operation for inventory management. The image of the recognized product is collated with a master file in which the product image and character information are associated with the product in advance, and if the recognized product image does not match the image registered in the master file, the recognized product A system is disclosed in which the character information extracted from the image is collated with the character information registered in the master file, and the product with the matching image information or character information is recorded in the inventory management file.

  Further, Patent Documents 2 to 4 disclose techniques for partially assisting the collation of articles by the user and reducing the work load. Patent Document 2 proposes a technique for identifying character information of an article and the position of the article by analyzing an image of a camera mounted on a moving body and detecting the position of the article based on the character information.

  Further, in Patent Document 3, when a plurality of articles attached with RFID tags are recognized by a portable RFID tag reader, the RFID tag according to the position and orientation of the RFID tag of the article determines whether or not there is a recognition failure. Furthermore, a technique for detecting an RFID tag recognition failure by comparing the number of RFID tags in an image with the number of RFID tags read by an RFID tag reader is proposed.

  Patent Document 4 is managed by a library by comparing the total weight of books that should be in the container in the library management data with the total weight actually measured in the container in the library. A technique for detecting whether there is an unknown book or a data error is disclosed.

Japanese Patent No. 3493007 Japanese Patent No. 4135556 JP 2008-65386 A JP 2005-67748 A

  Here, in order to reduce the burden on the user, it is preferable that an article matching operation for inventory management is automatically performed. However, when an article collation operation is performed based on an image obtained by photographing an article to be collated and an article image registered in advance for each article by the method of Patent Document 1, the appearance is similar to one image. When a plurality of article candidates to be extracted are extracted, the article corresponding to the image cannot be specified. Also, the method of Patent Document 2 cannot perform a matching process for inventory management when a plurality of candidate articles are extracted. Further, according to the methods of Patent Documents 3 and 4, although the number and the total weight of a plurality of articles can be used to collate with previously registered articles, the overall weight and the number of articles may be the same. For example, even if an article of a different type from an article registered in advance is included, it is not possible to distinguish between the articles, and therefore more accurate collation is required.

  The present invention has been made in view of the above circumstances, and when performing an article collation operation based on an image obtained by photographing an article to be collated and an image of an article registered in advance for each article, one article in the image It is an object to provide an article collation apparatus, an article collation method, and an article collation program that can collate more accurately when a plurality of article candidates are extracted.

  An article collation apparatus according to the present invention includes an article information storage unit that stores article information in which overall image information representing an overall appearance of each article is associated in advance for each of a plurality of different kinds of articles, An image obtained by photographing the object to be collated is acquired, a first collation unit that performs first collation based on the obtained image and article information, and a first collation by the first collation unit to the object to be collated When an article candidate group consisting of a plurality of article candidates extracted is extracted for a plurality of matching target articles, the number of matching target articles from which the article candidate group is extracted and each of the plurality of article candidate groups And a second collation unit that collates a plurality of article candidates included in the article candidate group in units of counties based on a first collation condition that the number of article candidates matches each other.

  An article collation method according to the present invention is an article collation method in an article collation apparatus, which acquires an image obtained by photographing a plurality of collation target articles, and obtains an image and a plurality of different types of articles for each article. The first collation step for performing the first collation based on the article information preliminarily associated with the entire image information representing the overall appearance of the article, and the first collation step extract the article to be collated. When an article candidate group including a plurality of article candidates is extracted for a plurality of matching target articles, the number of matching target articles from which the article candidate group is extracted and the number of each article candidate included in the plurality of article candidate groups And a second collation step for collating a plurality of article candidates included in the article candidate group in units of counties based on a first collation condition that the two coincide with each other.

  An article collation program according to the present invention causes a computer to execute the above method.

  In the article collation apparatus of the present invention, the “article” may be any tangible object, and can be suitably applied to, for example, a tangible article that can be displayed and sold. For example, the article can be any one of a recording medium storing predetermined data, a book, and a cosmetic.

  Examples of the recording medium storing the predetermined data include a commodity in which any one of audio, image, and computer software is recorded on a recording medium such as a magnetic medium or an optical medium by an arbitrary recording technique. Typical examples of recording media include DVD, BD, CD, memory card, magnetic tape, and the like.

  The “attribute” means a classification that can classify information belonging to an article, and may be classified from any viewpoint as long as information belonging to the article can be classified. For example, as an “attribute” in a predetermined type of attribute or a further type of attribute, the size, price, color histogram of the appearance of the article, such as the length of the article in a given direction, classification, manufacturer, publisher, year of manufacture , The release year, the author, the typeface included in the appearance of the article, and the number of colors included in the appearance of the article.

  The above-mentioned "matching a plurality of article candidates included in the article candidate group in units of counties" does not specify articles that individually correspond to the plurality of matching target articles from which the article candidate group is extracted, It is determined that the plurality of verification target articles from which the candidate article group has been extracted correspond to the multiple candidate articles included in the verification target article group, and the multiple verification target articles from which the candidate article group has been extracted are collectively collected. Means matching. As a result, the plurality of matching target articles from which the article candidate group has been extracted without individually matching the plurality of matching target articles from which the article candidate group has been extracted with the plurality of article candidates included in each matching target article group. For all, it is collectively determined that the corresponding article has been detected. Then, the management information table and the like are updated, assuming that a plurality of articles included in the collation target article group correspond to the collation target articles.

  Moreover, in the article collation apparatus according to the present invention, the article information is further associated with the first attribute information representing the attribute content in the first attribute representing the numerical value of a predetermined type related to the article for each article, The second collation unit extracts the first attribute information for each article candidate based on the article information, and the second collation that the first attribute information of the plurality of extracted article candidates belongs to a common predetermined range. Based on the conditions, it is preferable to collate a plurality of article candidates included in the article candidate group in units of counties. In this case, arbitrary attribute information representing a numerical value related to the article can be used as the first attribute information. For example, the first attribute information is preferably information representing the price of the article.

  In the article collating apparatus according to the present invention, the article information is further associated with second attribute information representing attribute contents in a second attribute representing a further predetermined type of numerical value related to the article for each article. The second verification unit extracts the second attribute information for each item candidate based on the item information, and sums the second attribute information of the plurality of item candidates included in the item candidate group for each item candidate group. It is preferable to collate a plurality of article candidates included in the article candidate group on a county basis based on a third matching condition that the value is calculated and the total value of the second attribute information matches. In this case, arbitrary attribute information that represents a numerical value related to the article can be used as the second attribute information. For example, the second attribute information indicates the predetermined direction of the article such as the width, height, and diameter of the article. The information is preferably information representing a length or a color histogram of the article.

  In the article collation apparatus according to the present invention, the article information is further associated with the third attribute information representing the attribute content in the third attribute of the article for each article, and the second collation unit includes the article information in the article information. Based on the fourth matching condition that the third attribute information is further extracted for each article candidate, and the combination of the third attribute information of the plurality of article candidates included in the plurality of article candidate groups matches. It is preferable to collate a plurality of article candidates included in the article candidate group on a county basis. In this case, arbitrary attribute information can be used as the third attribute information. For example, it is preferable that the third attribute information is identification information of an article.

  The first attribute information, the second attribute information, and the third attribute information may be different from each other, and all of the first to third attribute information are the same type of information. The two pieces of attribute information selected from the first to third attribute information may be the same type of information.

  According to the present invention, images obtained by photographing a plurality of items to be collated are acquired, and the acquired image and a plurality of different types of items are pre-corresponding to the entire image information representing the overall appearance of each item for each item. The first collation is executed based on the attached article information, and an article candidate group including a plurality of article candidates extracted for the collation target article is extracted for the plurality of collation target articles by the first collation. A plurality of items included in the item candidate group based on a first matching condition that the number of items to be verified from which the item candidate group is extracted and the number of item candidates included in the plurality of item candidate groups match each other. The second matching is performed to match the goods candidates of each item by county.

  Therefore, based on the first matching condition that the number of matching target articles from which the article candidate group is extracted matches the number of article candidates included in the plurality of article candidate groups, Can be collated together with a plurality of article candidates stored in advance. Moreover, it extracts as an article candidate based on the image of the photographed article and the entire image information in the article information, and based on the first matching condition among the article candidates extracted based on the image representing the overview. Since collation is performed, collation can be performed with higher accuracy than when the number of articles is simply compared.

The block diagram which shows schematic structure of the article collation system using one Embodiment of the article collation apparatus of this invention. The figure which shows an example of an article master table The figure which shows an example of the collation result stock table The flowchart for demonstrating the effect | action of the article collation system using one Embodiment of the article collation apparatus of this invention. Flow chart for explaining the second matching process The block diagram which shows schematic structure of one Embodiment of the article master table registration system which produces an article master table Flowchart for explaining the operation of the article master table registration system shown in FIG. Flowchart for explaining a method for acquiring partial feature information

  Hereinafter, an article collation system using an embodiment of an article collation apparatus and method and a program of the present invention will be described in detail with reference to the drawings. The article collation system of the present embodiment acquires image data obtained by photographing articles displayed on a display shelf, and collates the image data with inventory article information registered in advance in the article master table, thereby inventorying articles. Management processing is performed. FIG. 1 is a block diagram showing a schematic configuration of an article matching system 1 of the present embodiment.

  As shown in FIG. 1, the article collation system 1 according to the present embodiment photographs an article collation apparatus 20, a monitor 30 connected to the article collation apparatus, and an article collation apparatus 20 to photograph a plurality of articles to be collated. And the camera 10 for providing the article verification device 20 to the article verification apparatus 20, and the article verification apparatus 20 is connected to the article master table registration system 2 through the communication network so as to be accessible to the aforementioned article master table.

  The article collation apparatus 20 in the present embodiment is a computer used when a user confirms and / or edits an inventory table for managing the inventory status of articles, and includes an input device (not shown) including a keyboard and a mouse. Standard software such as a system is installed. The article verification device 20 is controlled by a program installed from a recording medium such as a CD-ROM, and functions as the article verification device 20 of the present embodiment. The program may be installed after being downloaded from a storage device of a server connected via a network such as the Internet.

  The article collation apparatus 20 acquires an image obtained by photographing an image representing one storage shelf displaying a plurality of articles, and stores an image acquisition unit 21 (image buffer) for storing different types of articles. A master database 25 (article information storage unit) that stores an article master table T1 (article information) in which overall image information representing the overall appearance of each article and attribute information representing the attributes of the article are associated in advance for each article. And a single product image extraction unit 22 (image extraction unit) that extracts individual products as single product images from images obtained by photographing a plurality of products to be collated, and overall image information of the registered products based on the product master table T1. The first verification unit 23 that compares the single item image with the single item image and matches the item corresponding to the single item image, and the item that has not been verified by the verification process by the first verification unit 23 Comprising a second matching unit 24 for further verification process the article registered in a star table T1, a communication unit 26 for connecting an article verification device 20 to the communication network.

  The master database 25 is a storage device configured by a known storage medium such as a hard disk, and stores a matching result inventory table T2 and a management inventory table T3 as shown in FIG. 1 together with the article master table T1 described above. To do. Hereinafter, prior to the article matching process of the article matching apparatus 20, the article master table T1, the matching result inventory table T2, and the management inventory table T3 will be described.

  FIG. 2 shows an example of the article master table T1. As shown in FIG. 2, the article master table T1 includes an article code (identification information) for identifying a plurality of articles, overall image information of the article specified by each article code, similar article code, attribute information, and This is associated with partial feature information.

  Here, the article may be any article as long as it is displayed on the display shelf and displayed and sold. Examples of such articles to be displayed and sold include cosmetics and books, DVDs and BDs on which video information is recorded, CDs on which music information is recorded, CD-ROMs on which software applications are recorded, memory cards, magnetics, etc. There is a recording medium product on which predetermined information is recorded such as a tape. In addition, the article is not limited to the commodity sold as described above, but may be a book displayed on a display shelf of a library.

  The article master table T1 of the present embodiment is created for any one of articles such as cosmetics, books, and DVDs as described above.

  The article code in the article master table T1 is identification information for identifying individual articles as described above. For example, when the article is a cosmetic, a JAN (Japanese Article Number) code is used. When the article is a book, an ISBN (International Standard Book Number) code is used. The article code is not limited to JAN or ISBN, and any article code may be used as long as each article can be identified. Since the same article code is assigned to the same article, for example, even if there are a plurality of identical articles in stock, only one article code table corresponding to the article exists.

  The entire image information represents the overall appearance of the article, and specifically is image data obtained by photographing the entire article from the front, rear, left and right with a camera or the like. The entire image information registered in the article master table T1 may be image data itself, or link information of a connection destination such as a URL (Uniform Resource Locator) of a data server in which the image data is stored or a directory of a storage folder. Further, the whole image information includes image data taken from the direction that can be the front direction when the article is displayed on the display shelf. However, for example, in the case of a cylindrical article, the whole image information is not limited to the front / rear / left / right direction. Image data from the direction may be included.

  The similar article code is an article code of an article having overall image information similar to the article specified by the article code.

  The attribute information is information in which a plurality of attributes of an article are associated with attribute contents of each attribute. Here, the “attribute” means a classification that can classify information belonging to the article, and may be classified from any viewpoint as long as the information belonging to the article can be classified. For example, when the article is a cosmetic, as shown in FIG. 2, for example, as shown in FIG. 2, the “classification” of the cosmetic such as “product name” of the cosmetic, “price” of the cosmetic, lotion, lipstick, foundation, etc. "(Category)", "Manufacturer" of cosmetics, "Production year" of cosmetics, "Size (height)" and "Size (width)" of cosmetics, and appearance images of products viewed from a predetermined direction (for example, in a box) For example, a “color histogram” extracted from the front image of the product box).

  For example, when the article is a book, the types of attributes include, for example, the “title” of the book, the “price” of the book, the “classification” of the book such as a paperback book, a book, a comic, a magazine, and the “ “Publisher”, “Series Number” of the book, “Volume” of the book such as Volume 1, Volume 2, Volume 3, “Number of Book” such as January, February, March, ”,“ Size ”of books such as A4, B5, B6, etc. classified according to a predetermined size standard,“ font ”such as Gothic, Mincho, etc., and predetermined direction for characters and symbols included in the appearance of articles There is a “color histogram” extracted from the appearance image of the book viewed from above.

  For example, when the article is a DVD, the types of attributes are, for example, “title” such as a movie recorded on the DVD, “price” of the DVD, action, animation, horror, documentary, etc. “Category” of published movies, “Publisher” issuing DVD, “Standard part number symbol” of DVD, “Volume” of DVD such as 1, 2, 3, DVD, “Issued year” of DVD, DVD There are “size (height)” and “size (width)” of the package, “color histogram” extracted from the appearance image of the DVD viewed from a predetermined direction, and the like.

  The “attribute content” in the attribute means specific information content in the attribute. For example, according to the above-described example, when the article is a copyrighted work such as a book, and the attribute is “classification”, a booklet, a book, a comic, a magazine, and the like correspond to the attribute contents. Further, when the attribute is “maker”, “publisher”, or “author”, an organization name such as a company or an individual name can be used as the attribute content. When the attribute is “size”, for example, A4, B5, B6 classified according to a predetermined size standard, or a predetermined numerical value or a predetermined numerical range classified according to a length or an area in a predetermined direction, etc. It can be. When the attribute is “production year” or “issue year”, a specific year such as 2012 or 2011 can be used as the attribute content. Further, when the attribute is “font” included in the appearance of the article, the type name of a specific typeface such as Gothic or Mincho can be used as the attribute content. When the attribute is “the number of colors” included in the appearance of the article, one color, two colors, three colors, or the like can be used as the attribute content. In addition, when the attribute is a “color histogram” included in the appearance of the article, a color histogram for a predetermined color such as RGB extracted from the entire image information of the image can be used as the attribute content. The color histogram registered in the article master table T1 may be the color histogram data itself, or a connection destination such as a URL (Uniform Resource Locator) of a data server in which the color histogram data is stored or a directory of a storage folder. Link information may be used.

  In addition, when the article is a music CD, for example, classic, pop music, movie music, and the like can be used as the attribute content in the classification. When the article is a moving picture DVD, for example, action, animation, horror, documentary, and the like can be attribute contents in the classification. Further, the classification of cosmetics can be attributed to the type of cosmetics such as lipstick, mascara, and eye shadow.

  In the above description, the attributes of cosmetics, books, DVDs, and CDs have been described. However, the present invention is not limited to this, and attribute information including various attributes and attribute contents is registered according to the type of article.

  In the article master table T1, the attribute information as described above is registered in association with each article. In the present embodiment, the article master table T1 includes an article that is a price associated with each article (first attribute information), an article width (second attribute information), and article identification information. Information indicating a code (third attribute information) is associated with each other, and these first to third attribute information are stored in a second collation unit 24 described later based on the article master table T1. Used for verification processing.

  The partial feature information is information of a partial range of the appearance of the article. Specifically, the above-described attribute contents appearing in a partial range of the appearance of the article and position information of a partial range in which the attribute contents appear are registered as partial feature information.

  In FIG. 2, the X coordinate, the Y coordinate, and the size when the horizontal direction is the X direction and the vertical direction is the Y direction are registered as position information of a part of the range. As the X and Y coordinates of a part of the range, when the part of the range is defined by a rectangular area, for example, the position coordinates of the upper left corner of the rectangular area or the center coordinates of the rectangular area are used. be able to. Also, as the size of the partial range, the size in the X direction and the size in the Y direction of the partial range are registered. Note that the position information of the partial range is relative positional information in the overall appearance of the article, and the attribute contents appearing in the partial range of the appearance of the article are directed in the front direction. Position information. The number of partial feature information is not necessarily one, and a plurality of combinations of attribute contents and position information may be registered.

  In the article master table T1, the article code and the above-described whole image information, similar article code, attribute information, and partial feature information are registered in association with each other for many articles sold in the market. . The article master table T1 is created by the article master table creation device 50 in the article master table registration system 2 to be described later, and the creation method will be described in detail later.

  FIG. 3 shows an example of the matching result inventory table T2. As shown in FIG. 3, the verification result inventory table T <b> 2 is a table in which each article code is associated with the inventory number of articles specified by the article code. The verification result inventory table T2 is updated based on the result of the verification process in the first verification unit 23 or the second verification unit 24 described above. Specifically, when one article candidate is extracted from the article master table T1 for the article corresponding to the single item image by the matching process, the number of inventory of the article code of the article in the matching result inventory table T2 is incremented by one. The verification result stock table T2 is updated.

  Similarly to the matching result inventory table T2, the management inventory table T3 is a table in which each article code is associated with the number of articles specified by the article code. It is updated every time an article is carried in and out. Specifically, each time an article is brought into a store, a library, or the like, the management inventory table T3 is incremented by one when the number of inventory corresponding to the article code of the article is incremented and the article is carried out. The stock quantity corresponding to the article code of the article is decremented by one and updated. Note that the delivery of an article at the store means that the article has been received, and the delivery of the article means that the article has been sold and taken out.

  That is, the management inventory table T3 represents the inventory status of articles managed in stores and libraries, and the matching result inventory table T2 represents the inventory status of articles actually present in the stores and libraries. It is. The management stock table T3 and the matching result stock table T2 are essentially identical when the above-described matching processing is completed. However, for example, if an article is stolen or there is a mistake in updating the management inventory table T3 at the time of carrying in or carrying out the article, these tables will not match. The verification result stock table T2 and the management stock table T3 are displayed on the monitor 30.

  Next, the operation of the article matching system 1 of the present embodiment will be described with reference to the flowchart shown in FIG.

  First, prior to the article collation process, the user takes an image of articles arranged on the display shelf using the camera 10. Specifically, here, the shooting range of the camera 10 is set so that the entire display shelf including a plurality of shelves on which a plurality of products are displayed is included, and shooting is performed. And the image data image | photographed with the camera 10 is acquired by the image acquisition part 21 of the goods collation apparatus 20 connected to the camera 10 by wire or radio | wireless, and is once stored.

  Then, when the user activates the article collation processing software, selects an image obtained by photographing the above-described one display shelf stored in the image acquisition unit 21 using the input device, and selects the article collation processing start button. In response to these designation operations and selection operations, the article matching device 20 starts the article matching process.

  First, the single product image extraction unit 22 reads out the image data stored in the image acquisition unit 21, and extracts and classifies single product images (single product image data) of each article from the read image data (S10). . Here, the single product image extraction unit 22 acquires one image obtained by photographing a plurality of articles to be collated from the image acquisition unit 21, and uses the well-known template matching method to obtain an area that matches the external shape of the article stored in advance. By using the article region to be verified, the individual article region is recognized from this image, and single product image data of each article is extracted. It should be noted that various other known methods can be used for the extraction of single item image data.

  Next, the first matching unit 23 performs first article matching processing on the image data of the entire image information registered in the article master table T1 and all the extracted single item image data (S12).

  Here, the 1st collation part 23 performs template matching with the single-item image of the goods for collation, and the image data of the whole image information of the goods registered into the goods master table T1, and evaluates the correlation between known images. A value representing the similarity is calculated using the evaluation function. Note that the process of calculating the image similarity by the first matching unit 23 may use any known method capable of calculating the image similarity.

  When the calculated value is judged based on the threshold condition, and only one piece of image data of the whole image information having a similarity (similar) greater than (similar to) a predetermined criterion represented by the threshold is extracted. The article corresponding to the image data is determined. When one piece of image data of the entire image information is extracted for each single piece of image data (S14, YES), the first matching unit 23 checks the matching result inventory table T2 on the master database 25. Reflect (output) the verification result in. That is, in the matching result inventory table T2, for each extracted whole image, the number of stocks corresponding to the article code of the article corresponding to the extracted whole image is incremented by the number of times the whole image has been extracted, The verification result inventory table T2 is updated (S22). Next, the updated verification result inventory table T2 is displayed on the monitor 30 so that the user can check (S24).

  On the other hand, when a plurality of article candidates are extracted by the first matching process (S14, NO, S16, a plurality of article candidates), the second matching unit 24 performs the second matching process (S18). .

  Here, the second matching process (S18) of FIG. 4 will be described in detail with reference to the flowchart shown in FIG.

  The second collation unit 24 is configured when an article candidate group including a plurality of article candidates extracted for the collation target article is extracted for a plurality of collation target articles by the first collation in the first collation unit 23. Then, it is determined whether or not the first matching condition that the number of matching target articles from which the article candidate group is extracted and the number of article candidates included in the plurality of article candidate groups match each other is satisfied (S40).

  Here, a first example of the verification target product group G (A, B, C) including the verification target products from which a plurality of product candidates have been extracted (the verification target product group has been extracted) is shown as an example. The collation process by 2 collation part 24 is demonstrated. Note that the article candidate group Ga (a1, a2, a3) is extracted for the single item image data including the verification target article A, and the article candidate group Gb (b1, b2, b3) is extracted for the single item image data including the verification target article B. It is assumed that the item candidate Gc (c1, c2, c3) is extracted for the single item image data including the verification target item C. Further, a1, a2, a3, b1, b2, b3, c1, c2, and c3 represent article candidates included in the article candidate group.

  In the above example, the number of matching articles in the matching target article group G is three (A, B, C), and the number of article candidates in the article candidate group Ga for the matching target article A is three (a1, a2, a3). Similarly, the number of article candidates in the article candidate group Gb for the matching target article B is b1, b2, and b3, and the number of article candidates in the article candidate group Gc for the matching target article C is c1, c2, Three cases of c3. For this reason, in the above example, the number of matching articles from which a plurality of article candidates are extracted and the number of article candidates included in each article candidate group are common, and the first matching condition is satisfied.

  If the first verification condition is satisfied, the second verification unit 24 further extracts an article code, which is third attribute information, for each article candidate based on the article master table T1, and a plurality of articles It is further determined whether or not the fourth matching condition that the combination of the attribute information of the article codes of the plurality of article candidates included in the candidate group matches is satisfied. When the first verification condition or the fourth verification condition is not satisfied (S40, No), the second verification unit 24 stores the image data of the verification target article as unmatched image data (S46). 2 collation processing is completed.

  Next, when the fourth matching condition is satisfied, that is, the article candidate a1, a2, a3 of the article candidate group Ga for the matching target article A, and similarly, the article candidate group Gb for the matching target article B If the combination of each of the article candidates b1, b2, and b3 and the article candidates c1, c2, and c3 of the article candidate group Gc for the matching target article C match (S40, Yes), the second matching The unit 24 extracts the price that is the first attribute information for each article candidate based on the article master table T1, and the first attribute information of the extracted plurality of article candidates belongs to a predetermined range in common. It is determined whether or not the collation condition 2 is satisfied (S42).

The second collation unit 24 is configured to obtain article candidates a1, a2, and a3 for the collation target article A, article candidates b1, b2, and b3 for the collation target article B, and article candidates c1, c2, and c3 for the collation target article C. Each price is acquired from the article master table T1. And the 2nd collation part 24 judges whether the price of the some goods candidate which belongs to goods candidate group Ga satisfy | fills following formula (1). In the following formula (1), t of the candidate article is a threshold value, and it can be determined that the price of the plurality of candidate articles is more approximate as the threshold value t is set to a value closer to 1. . For example, the threshold value t is preferably set to a value of 0.8 or more. Instead of the following formula (1), the second collation condition may be defined as the first attribute information of a plurality of article candidates all matching (belonging to a common predetermined range).
Min {a1, a2, a3} / Max {a1, a2, a3} ≧ t (1)

  Here, the threshold value t = 0.8, the prices of a1, a2, a3 are all the same, the prices of b1, b2, b3 are all the same, and the prices of c1, c2, c3 are all the same It is assumed that the formula (1) is satisfied. Note that the second matching unit 24 may determine whether or not only the arbitrary group selected from the article candidate group satisfies the second matching condition, and all the groups selected from the article candidate group are determined. It may be determined whether or not the second matching condition is satisfied. Further, the second collation condition may be determined for each of a plurality of types of first attribute information as to whether or not each first attribute information included in each article candidate group belongs to each predetermined range. Good.

  When the second matching condition is not satisfied (S42, No), the second matching unit 24 stores the image data of the matching target article as unmatched image data (S46), and ends the second matching process. To do. When the second collation condition is satisfied (S42, Yes), the second collation unit 24 uses the article master table T1 to determine the width of the article as the second attribute information for each article candidate. Is calculated for each item candidate group, the total value of the widths of the plurality of item candidates included in the item candidate group is calculated, and the third matching condition that the total value of the second attribute information matches is satisfied It is determined whether or not (S44).

  The second verification unit 24 calculates the total value of the widths of the product candidates a1, a2, and a3 included in the product candidate group Ga and the total value of the widths of the product candidates b1, b2, and b3 included in the product candidate group Gb. If the total value of the widths of the article candidates c1, c2, c3 included in the article candidate group Gc matches, it is determined that the third matching condition is satisfied (S44, Yes), and the second matching is performed. The process ends. On the other hand, when the third matching condition is not satisfied (S44, No), the second matching unit 24 stores the image data of the matching target article as unmatched image data (S46), and performs the second matching process. Exit.

  In addition, when the third matching condition is that the total value of the widths of the article candidates (second attribute information) included in the article candidate group matches the total value of the widths of the matching target articles (second attribute information). It may be defined by deformation. In this case, the second matching unit 24 extracts the widths of the matching target articles A, B, and C from the single item image data by a known method, and the total value of the widths of the matching target articles A, B, and C, It is determined whether or not the total value of the article candidates included in the article candidate group matches.

  In addition, since specific numerical values such as each width may cause an error depending on the width measurement conditions and extraction conditions, the above "total values match" is only when the values of each width exactly match Not including the case where the total value is within the error range.

  In addition, a color histogram for each RGB color may be used as the second attribute information. Moreover, you may make it discriminate | determine whether the total value of the 2nd attribute information contained in each goods candidate group corresponds about 3rd collation conditions about 2nd kind attribute information. For example, the third matching condition is that (i) the total value of the widths of the product candidates included in each product candidate group matches, and (ii) the total values of the color histograms included in each product candidate group match. It may be set.

  As shown in FIG. 4, in the above-described second matching process, when all the matching conditions are satisfied, the second matching unit 24 performs one-to-one correspondence between the elements of the matching target article group G and the article candidate group Ga. Although the correspondence relationship is unknown, it is determined that the matching object group G (A, B, C) is the same as the candidate article group Ga (a1, a2, a3), and the matching object group G (A, B , C), the articles corresponding to each matching target article are collectively determined (S20, YES), and the matching result is reflected (output) in the matching result inventory table T2 on the master database 25. That is, the second verification unit 24 updates the verification result inventory table T2 by incrementing the inventory number corresponding to the article code of each article a1, a2, a3 included in the article candidate group Ga by one (S22). Next, the updated matching result inventory table T2 is displayed on the monitor 30 so that the user can confirm the result (S24).

  On the other hand, if any of the matching conditions is not satisfied by the second matching process (S20, NO), the monitor 30 indicates that the stored unmatched image data and the corresponding article could not be extracted. It is displayed (S28). Then, the user who has confirmed the monitor 30 directly confirms the appearance and the article code of the article to be collated manually, if necessary, and reflects the collation result on the master database 25 by manual operation. The result stock table T2 is updated (S22). Then, the updated verification result inventory table T2 is displayed on the monitor 30 (S24).

  In addition, not only when a corresponding article is not extracted by the second matching process, but when a corresponding article candidate is not extracted in the first matching process described above (S16, No), The image data is stored as unmatched image data (S26). Then, the stored unmatched image data and the fact that the corresponding article candidate could not be extracted is displayed on the monitor 30 (S28). Then, the user who has confirmed the monitor 30 directly confirms the appearance and the article code of the article to be collated manually, if necessary, and reflects the collation result on the master database 25 by manual operation. The result stock table T2 is updated (S22). Then, the updated verification result inventory table T2 is displayed on the monitor 30 (S24).

  Further, when the matching result inventory table T2 is displayed, the difference in the inventory number of articles between the matching result inventory table T2 obtained by the article matching process according to the present embodiment and the management inventory table T3 stored in advance is calculated. It is preferable to calculate and display the difference in the inventory quantity of the articles so as to be visible. Further, the process of displaying the updated matching result inventory table T2 on the monitor 30 may be performed after all the matching processes are completed as in the above example, or after each matching process is completed. May be.

  According to the present embodiment, images obtained by photographing a plurality of verification target articles are acquired, and for the acquired images and a plurality of different types of articles, overall image information representing the overall appearance of each article is obtained for each article. The first collation is executed based on the article information associated in advance, and an article candidate group including a plurality of article candidates extracted with respect to the collation target article by the first collation is a plurality of collation target articles. When extracted, the number of article candidates to be collated is included in the article candidate group based on the first matching condition that the number of article candidates included in the plurality of article candidate groups matches each other. The second matching is performed to match a plurality of article candidates in units of counties.

  For this reason, a plurality of article candidate groups extracted by a relatively simple process of comparing the number of target articles for which the article candidate groups have been extracted and the number of article candidates included in the plurality of article candidate groups. It is possible to appropriately collate the object to be collated with a plurality of article candidates stored in advance. In addition, since collective collation is performed in units of counties based on the first collation condition, a process of extracting individual corresponding articles from among the article candidates for a plurality of collation target articles from which candidate article groups are extracted is performed. Therefore, it is possible to prevent the calculation load of the collation process from increasing more than necessary. In addition, an article in which the image of the photographed article and the entire image information in the article information are similar is extracted as an article candidate, and based on the first matching condition among the article candidates in which the image representing the overview is similar Therefore, the collation can be performed with higher accuracy than when the number of articles is simply compared.

  Moreover, in the said embodiment, the 1st attribute information showing the attribute content in the 1st attribute showing the numerical value of the predetermined kind relevant to articles | goods for every article | item is further matched, and 2nd collation is carried out. Based on the article information, the first attribute information is extracted for each article candidate, and the second matching condition that the first attribute information of the plurality of extracted article candidates belongs to a common predetermined range. On the basis of this, since a plurality of article candidates included in the article candidate group are collated on a county basis, the articles can be collated more accurately.

  In addition, articles of the same series, such as books issued over a plurality of volumes, may have a similar appearance. Since articles of the same series are generally arranged close to each other, when a plurality of articles to be collated of a predetermined series are included in a captured image, a plurality of articles of the same series having a similar overview However, it is considered that there is a high possibility that a plurality of predetermined series of matching target articles will be extracted as article candidates. In addition, articles of the same series often have the same or similar attribute information. For this reason, by using the second matching condition that the first attribute information of the plurality of article candidates belongs to a common predetermined range for matching, group unit matching can be suitably performed, and the load of matching processing is reduced. There is a high possibility that it can be suitably reduced. For example, in books, DVDs, etc., articles in the same series often belong to the same price range. Therefore, when the first attribute information is information representing the price of an article as in this embodiment, it is preferable. Group-based verification can be performed.

  Moreover, in the said embodiment, the 2nd attribute information showing the attribute content in the 2nd attribute showing the numerical value of the further predetermined kind relevant to articles | goods for every article | item is further matched with 2nd attribute information. The collation unit extracts second attribute information for each item candidate based on the item information, and calculates a total value of the second attribute information of a plurality of item candidates included in the item candidate group for each item candidate group. In addition, since the plurality of article candidates included in the article candidate group are collated in units of counties based on the third collation condition that the total value of the second attribute information matches, the collation of articles can be performed more accurately. It can be performed.

  Further, when the second attribute information is information indicating the length of the article in a predetermined direction such as the width, height, and diameter of the article, or the color histogram of the article, a plurality of items can be obtained by relatively simple processing. The article candidate can be collated more accurately.

  Moreover, in the said embodiment, the 3rd attribute information showing the attribute content in the 3rd attribute of an article | item is further matched for article | item information for every article | item, A 2nd collation part is based on article | item information, Further, the third attribute information is further extracted for each article candidate, and the article candidate is also based on the fourth matching condition that the combinations of the third attribute information of the plurality of article candidates included in the plurality of article candidate groups match. Since the plurality of article candidates included in the group are collated in units of counties, the articles can be collated more accurately.

  Further, since the third attribute information is the identification information of the article, it is possible to suitably determine the combination of article candidates.

  Also, in stores and libraries, related articles such as articles of the same series are often arranged close to each other, and when the appearance of articles of the same series are similar, one single piece of image data Since a plurality of article candidates may be extracted, the image obtained by shooting the matching target article used in the above embodiment is shot in a range in which the matching target articles of the same series are often arranged close to each other. It is preferable to use the above. From this viewpoint, as in the above-described embodiment, an image obtained by photographing a range such as one storage shelf or a display stand can be used. In addition, the article matching process used in the above embodiment may be performed in units of one image or in units of a plurality of images.

  In the above embodiment, the article can be any commodity that can be displayed and sold. For example, when the article is any one of a recording medium product such as CD, DVD, BD, and magnetic tape in which at least one of voice, image, and computer software is recorded by an arbitrary recording method, a book, and cosmetics, this implementation The article collation processing by the form article collation apparatus 20 can be suitably applied.

  Further, as in the above embodiment, the second matching unit 24 performs the matching process based on the second matching condition (S42), the matching process based on the third matching condition (S44), and the matching based on the fourth matching condition. All of the processes may be performed, or only one or more collation processes arbitrarily selected from the collation processes based on the second to fourth collation conditions may be performed, and based on the second to fourth collation conditions All the verification processes may be omitted. When two or more collation processes selected from the collation processes based on the first to fourth collation conditions are performed, the selected collation processes may be performed in an arbitrary order, or may be performed simultaneously. Good. In addition, any method can be used to determine whether or not the first to fourth collation conditions are satisfied. Arbitrary attribute information representing numerical values related to the article can be used as the first and second attribute information, and arbitrary attribute information related to the article can be used as the third attribute information. The first attribute information, the second attribute information, and the third attribute information may be different from each other, and all of the first to third attribute information are the same type of information. The two pieces of attribute information selected from the first to third attribute information may be the same type of information.

  Moreover, in the said embodiment, the article collation apparatus 20 may apply the further collation process by a well-known various collation method with respect to the single item image data which could not be collated by the 2nd collation process.

  Next, an article master table registration system for creating the article master table T1 described above will be described. FIG. 6 is a block diagram illustrating a schematic configuration of the article master table registration system 2.

  The article master table registration system 2 of this embodiment includes a camera 40, an article master table creation device 50, and a monitor 70, as shown in FIG.

  The camera 40 captures the overall appearance of the article to be registered in the article master table T1, and registers it in the article master table T1 based on image data obtained by capturing the overall appearance of the article by the camera 40. The entire image information is acquired. Therefore, the imaging | photography with the camera 40 is performed from the front-back, left-right direction of the goods for registration, for example. If the image data includes a barcode image representing an article code, an article code registered in the article master table T1 is acquired based on the barcode image. If the image data of the overall appearance of the registration target article does not include a barcode image representing the article code, the part including the barcode image is separately photographed by the camera 40. Further, the barcode image may be separately read by a barcode reader. In addition, about imaging | photography of the registration object goods, it is performed for every goods.

  In the present embodiment, the entire image information and the article code of the article to be registered are acquired using the image data taken by the camera 40. For example, when the article is a book, You may make it acquire image data using a scanner.

  In addition, image data including the overall appearance of the article and image data including a barcode image are not necessarily acquired by photographing with a camera or photoelectric reading with a scanner. For example, in the process of editing a book, Image data of a front cover, a back cover, and a back cover created in advance may be acquired, and the entire image information and article code may be acquired based on the image data.

  As shown in FIG. 6, the article master table creation device 50 includes an image acquisition unit 51, an image region extraction unit 52, a similarity determination unit 53, a partial region extraction unit 54, a character recognition unit 55, an article code region extraction unit 56, an article. A code recognition unit 57, an article attribute acquisition unit 58, an article attribute information database 59, a character match determination unit 60, a master database 61, and a communication unit 63 are provided.

  The image acquisition unit 51 acquires image data of an article to be registered photographed by the camera 40 and temporarily stores it in a storage medium such as a semiconductor memory or a hard disk.

  The image area extraction unit 52 reads the image data of the registration target article from the image acquisition unit 51, and acquires the article area image data by deleting the image data of the background of the article from the image data. Various known methods can be used as a method for acquiring the article region image data. The article region image data (for example, front / rear / left / right image data) acquired by the image region extraction unit 52 is registered in the article master table 62 as the entire image information of the article to be registered.

  The similarity determination unit 53 acquires the article region image data acquired by the image region extraction unit 52, and collates the article region image data with the entire image information of a plurality of articles already registered in the article master table 62. Then, an article code having overall image information including image data similar to the article area image data of the article to be registered is acquired as a similar article code. The matching between the article region image data and the entire image information of the registered article may be performed, for example, by calculating the similarity using template matching. The similar article code acquired by the similarity determining unit 53 is registered in the article master table 62 as the similar article code of the registration target article.

  The partial region extraction unit 54 receives the article region image data acquired by the image region extraction unit 52, and based on the article region image data, the above-described attribute content of the registration target article may appear. The region is extracted.

  Specifically, the partial region extraction unit 54 extracts a region where character information appears in the article region image data as a partial region. For example, the partial area extraction unit 54 binarizes the article area image data, performs labeling on the binarized data, and extracts a mass area where character information is supposed to exist based on the labeling information as a partial area To do. Note that the character information here includes not only hiragana, katakana, kanji and English characters, but also numbers and symbols. In addition to a plurality of characters, one character or the like is included.

  The extraction method of the character information area (partial area) is not limited to this, and various known methods can be used. Also, the number of partial areas is not limited to one, and when a plurality of character information areas are extracted, the plurality of character information areas may be extracted as partial areas. The labeling data of the partial area extracted by the partial area extraction unit 54 is output to the character recognition unit 55.

  The character recognition unit 55 performs character recognition processing based on the labeling data in the partial region extracted by the partial region extraction unit 54 and recognizes character information existing in the partial region. Since the character recognition process is already known, a detailed description thereof will be omitted. The character information recognized by the character recognition unit 55 is output to the character match determination unit 60.

  The article code area extraction unit 56 reads image data of a registration target article from the image acquisition unit 51, and extracts an area where a barcode image representing an article code exists from the image data as an article code area. As for the extraction of the area where the barcode image exists, an area where a plurality of line segments of a predetermined length are arranged may be extracted, but various known methods can be used for the extraction method. Information on the article code area extracted by the article code area extraction unit 56 is output to the article code recognition unit 57.

  The article code recognizing unit 57 acquires the article code of the article to be registered by recognizing a barcode image existing in the article code area based on the inputted article code area information. The article code acquired by the article code recognition unit 57 is registered in the article master table 62 as the article code of the article to be registered.

  The article attribute acquisition unit 58 acquires the article code recognized by the article code recognition unit 57, and acquires the attribute of the registration target article and its attribute contents from the article attribute information database 59 based on the contents of the article code. It is. As described above, the type of attribute information includes, for example, “series number”, “number of volumes”, “number”, “standard product number symbol” or “year of manufacture”. ”,“ Number of turns ”,“ number ”,“ standard part number symbol ”or“ production year ”. The attributes acquired by the article attribute acquisition unit 58 and their attribute contents are registered in the article master table 62 as attribute information of the registration target article. The attribute information of the registration target article is not limited to that acquired based on the article code, and may be acquired by, for example, setting input using an input unit by the user. The attribute content acquired by the article attribute acquisition unit 58 is also output to the character match determination unit 60.

  Then, the character match determination unit 60 compares the character information recognized by the character recognition unit 55 with the attribute content acquired from the article attribute acquisition unit 58, and if they match, the character information is represented by the character information. The attribute contents to be registered and the position information of the partial area in which the character information exists are registered in the article master table 62 as the partial feature information of the article to be registered. Specifically, for example, when both the character information recognized by the character recognition unit 55 and the content of the attribute information acquired from the article attribute acquisition unit 58 are “222333”, the “222333” The type of attribute information of “222333” (for example, the series number) and the position information of the partial area where the character information of “222333” exists are registered in the article master table 62 as the partial feature information of the article to be registered. . On the other hand, the character information recognized by the character recognition unit 55 is compared with the attribute content acquired from the article attribute acquisition unit 58. If they do not match, the character match determination unit 60 converts the character information into partial information. The feature information is not registered.

  The article attribute information database 59 stores attributes and attribute contents represented by various article codes.

  The master database 61 stores an article master table 62 for registered articles. The details of the article master table 62 are the same as the article master table T1 stored in the master database 25 of the article collating apparatus 20 described above. The master database 61 may store image data 62a of the entire image information of each article in the article master table 62.

  The article master table 62 in the article master table creation device 50 is updated each time a new registration target article table is registered. Then, the article master table 62 in the article master table creation device 50 is output to the article matching device 20 of the article matching system 1 via the communication unit 63 and the communication network in response to a request from the article matching device 20 to create an article master table. The article master table T1 is updated by overwriting the contents of the article master table 62 in the apparatus 50 on the article master table T1 in the article collating apparatus 20. The article master table T1 of the article verification device 20 may be updated every time the article master table 62 of the article master table creation device 50 is updated, or may be periodically updated every predetermined period. Alternatively, it may be performed at an arbitrary timing in accordance with an update instruction input by the user.

  The monitor 70 displays the article master image 62, the article region image data acquired by the image region extraction unit 52, and the like.

  Next, the operation of the article master table registration system 2 of the present embodiment will be described with reference to the flowcharts shown in FIGS.

  First, an article to be registered is installed at a predetermined position by the user (S50). Then, the entire appearance of the article to be registered is photographed by the user using the camera 40 (S52). At this time, if the image data of the overall appearance of the article does not include the barcode image representing the article code, the camera 40 separately captures the portion including the barcode image.

  Next, the image data photographed by the camera 40 is acquired and temporarily stored by the image acquisition unit 51 of the article master table creation device 50 connected to the camera 40 by wire or wirelessly.

  The image data acquired by the image acquisition unit 51 is output to the article code area extraction unit 56, and the article code area extraction unit 56 defines the area where the barcode image representing the article code is present from the input image data as the article code area. (S54).

  Information on the article code area extracted by the article code area extraction unit 56 is output to the article code recognition unit 57. The article code recognition unit 57 stores the information in the article code area based on the inputted article code area information. Is recognized (S56), and the article code of the article to be registered is acquired (S58). The article code acquired by the article code recognition unit 57 is registered in the article master table 62 as the article code of the article to be registered.

  Next, the image data acquired by the image acquisition unit 51 is output to the image region extraction unit 52, and the image region extraction unit 52 deletes the image data of the background of the article from the input image data, thereby performing the article region image. Get the data. Then, the article region image data (for example, front / rear / left / right image data) acquired by the image region extraction unit 52 is registered in the article master table 62 as the whole image information of the registration target article (S60).

  The article region image data acquired by the image region extraction unit 52 is output to the similarity determination unit 53, and the similarity determination unit 53 includes a plurality of pieces of article region image data that have been input and a plurality of items already registered in the article master table 62. The product code having the overall image information including image data similar to the product region image data of the registration target product is acquired as a similar product code (S62). The similar article code acquired by the similarity determining unit 53 is registered in the article master table 62 as the similar article code of the registration target article.

  Next, the article code recognized by the article code recognition unit 57 is output to the article attribute acquisition unit 58, and the article attribute acquisition unit 58 stores the registration target article from the article attribute information database 59 based on the contents of the article code. An attribute and its attribute content are acquired (S64). The attributes acquired by the article attribute acquisition unit 58 and their attribute contents are registered in the article master table 62 as attribute information of the registration target article.

  Further, partial feature information is acquired for the registration target article. The operation of acquiring the partial feature information will be described with reference to the flowchart shown in FIG.

  First, the article region image data acquired by the image region extraction unit 52 is input to the partial region extraction unit 54, and the partial region extraction unit 54 performs a character in the article region image data based on the input article region image data. The area where the information appears is extracted as a partial area (S70).

  Then, the labeling data of the partial region extracted by the partial region extraction unit 54 is output to the character recognition unit 55, and the character recognition unit 55 performs character recognition processing based on the labeling data in the partial region, and performs the character recognition processing in Is recognized (S72). The character information recognized by the character recognition unit 55 is output to the character match determination unit 60.

  On the other hand, the attribute content acquired based on the item code in the item attribute acquisition unit 58 is also output to the character match determination unit 60.

  Then, the character match determination unit 60 compares the character information recognized by the character recognition unit 55 with the attribute content acquired from the article attribute acquisition unit 58, and if they match, the character information is represented by the character information. The registered attribute contents and the position information of the partial area where the character information exists are registered in the article master table 62 as the partial feature information of the registration target article (S76). On the other hand, the character information recognized by the character recognition unit 55 is compared with the attribute content acquired from the article attribute acquisition unit 58. If they do not match, the character match determination unit 60 converts the character information into partial information. It is not registered as feature information.

  As described above, a new registration target article is registered in the article master table 62.

  According to the article master table registration system 2 of the above-described embodiment, the attribute information and position information of the partial feature information can be extracted with high accuracy and simplicity.

  Note that the present invention is not limited to this embodiment, and some or all of the constituent elements of the clinical information display apparatus may be configured by one workstation, and one or more connected via a network It may be configured by a workstation, a server, and a storage device. Each device is controlled by a program for displaying clinical information of the present specification installed from a recording medium such as a CD-ROM. The program may be installed after being downloaded from a storage device of a server connected via a network such as the Internet.

  Moreover, each embodiment mentioned above shows the one aspect | mode of this invention to the last, and a deformation | transformation and application are arbitrarily possible within the range which does not deviate from the meaning of this invention.

DESCRIPTION OF SYMBOLS 1 Article collation system 2 Article master table registration system 10, 40 Camera 20 Article collation apparatus 21 Image acquisition part 22 Single item image extraction part (image extraction part)
23 1st collation part 24 2nd collation part 25 Master database 30, 60 Monitor 50 Goods master table preparation apparatus T1 Goods master table T2 Check result stock table T3 Management stock table

Claims (10)

For a plurality of different types of articles, an article information storage unit that stores article information in which overall image information representing the overall appearance of each article is associated with each article in advance,
A first verification unit that acquires an image of a plurality of verification target articles, and performs a first verification based on the acquired image and the article information;
By the first matching in the first matching portion, when the article candidate group including a plurality of object product candidates extracted to the checking target article, which has been extracted for a plurality of the verification target article, the article candidate The plurality of articles included in the article candidate group based on a first matching condition that the number of articles to be collated from which the group is extracted and the number of article candidates included in the plurality of article candidate groups match each other. An article collation apparatus comprising: a second collation unit that collates candidates in units of groups . The article information is further associated with first attribute information representing attribute contents in a first attribute representing a predetermined type of numerical value related to the article for each article,
The second verification unit extracts the first attribute information for each article candidate based on the article information, and the first attribute information of the plurality of extracted article candidates is within a predetermined range in common. 2. The article collating apparatus according to claim 1, wherein the plurality of article candidates included in the article candidate group are collated in units of groups based also on a second collation condition that they belong.   3. The article collation apparatus according to claim 2, wherein the first attribute information is information representing a price of the article. The article information is further associated with second attribute information representing attribute content in a second attribute representing a numerical value of a further predetermined type related to the article for each article,
The second matching unit extracts the second attribute information for each of the article candidates based on the article information, and for each of the article candidate groups, the plurality of article candidates included in the article candidate group. A total value of the second attribute information is calculated, and based on a third matching condition that the total value of the second attribute information matches, the plurality of article candidates included in the article candidate group are grouped The article collating apparatus according to claim 1, wherein collation is performed using the following method.   5. The article collation apparatus according to claim 4, wherein the second attribute information is information representing a length of the article in a predetermined direction or a color histogram of the article. The item information is further associated with the third attribute information representing the attribute content in the third attribute of the item for each item,
The second matching unit further extracts the third attribute information for each of the article candidates based on the article information, and the third attributes of the plurality of article candidates included in the plurality of article candidate groups. 6. The system according to claim 1, wherein the plurality of article candidates included in the article candidate group are collated in a group unit based on a fourth collation condition that the combination of information matches. The article verification apparatus according to the description.   The article collation apparatus according to claim 6, wherein the third attribute information is identification information of the article.   The article collating apparatus according to any one of claims 1 to 7, wherein the article is any one of a recording medium on which predetermined data is recorded, a book, and a cosmetic. An article collation method in an article collation apparatus,
Acquiring an image obtained by photographing a plurality of collation target object, an image in which the acquired different for a plurality of articles of the type previously correlated article the whole image information representing the overall appearance of each article for each said article A first matching step for performing a first matching based on the information;
By the first collation step, the collation target object candidate group including a plurality of object product candidates extracted relative article, when it is extracted for a plurality of the verification target article, the article group of candidates are extracted the verification and the number of target object, a plurality of the article candidate plurality of articles candidate groups units contained in the article candidate group based on the first matching condition that each article number of candidates are matched with each other included in the group was The article collation method characterized by having the 2nd collation step collated by. On the computer,
Acquiring an image obtained by photographing a plurality of collation target object, an image in which the acquired different for a plurality of articles of the type previously correlated article the whole image information representing the overall appearance of each article for each said article A first matching step for performing a first matching based on the information;
By the first collation step, the collation target object candidate group including a plurality of object product candidates extracted relative article, when it is extracted for a plurality of the verification target article, the article group of candidates are extracted the verification and the number of target object, the group of the plurality of articles candidates included in the article candidate group based on the first matching condition that the number of each article candidates included in a plurality of said article candidate group coincide with each other were An article collation program that executes a second collation step for collating in units.

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