Patent Publication Number: US-9424480-B2

Title: Object identification using optical code reading and object recognition

Description:
RELATED APPLICATIONS 
     This application claims priority benefit under 35 U.S.C. §119(e) of U.S. Provisional Patent Application No. 61/636,538, filed Apr. 20, 2012, which is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     The field of this disclosure relates generally to systems and methods of identifying objects, and more particularly, but not exclusively, to data reading (e.g., reading optical codes) and object recognition. 
     Various approaches have been used to identify objects, such as items or merchandise, in retail applications. One approach involves the use of optical codes that encode useful, optically-readable information about the objects to which they are attached or otherwise associated. An optical code is essentially a machine-readable representation of information in a visual format. Some optical codes use a dark ink on a white substrate to create high and low reflectance upon scanning or reading of the optical code. For the purposes of the present description, the terms scanning and reading may be used interchangeably to connote acquiring data associated with an optical code. Likewise, optical code scanners and optical code readers may be used interchangeably to connote devices used to acquire (e.g., scan or read) data associated with an optical code. 
     Optical codes have various symbologies, such as UPC, Code 39, Code 128, and PDF417, that encode information data characters (or codewords as in the case of PDF417) and optional overhead characters according to a particular sequence of bars and spaces that may have varying widths. Perhaps the best example of an optical code is the barcode. Barcodes are ubiquitously found on or associated with objects of various types, such as the packaging of retail, wholesale, and inventory goods; retail product presentation fixtures (e.g., shelves); goods undergoing manufacturing; personal or company assets; and documents. By encoding information, a barcode typically serves as an identifier of an object, whether the identification be to a class of objects (e.g., containers of milk) or a unique item. 
     Various types of optical code readers, such as manual readers, semi-automatic readers, and automated readers, are available to decode the information encoded in optical codes. In a manual reader (e.g., a hand-held type reader, a fixed-position reader), a human operator positions a target object relative to the reader to read the optical code associated with the target object. In a semi-automatic reader, either checker-assisted or self-checkout, objects are moved usually one at a time by the user into or through a read zone of the reader and the reader then reads the optical code on the object. In an automated reader (e.g., a tunnel scanner, a portal scanner), the target object is automatically positioned (e.g., via a conveyor) relative to the reader to read the optical code on the target object. In manual, semi-automatic, and automated readers, an error (e.g., a no-code exception) may occur when the reader attempts to decode the optical code on the target object. For example, the reader may be unsuccessful in decoding the entire optical code of the target object because part of the optical code is blocked or otherwise obscured from the view of the reader. When such an error occurs, an operator may have to manually enter (e.g., via a keyboard) an identification number (e.g., a UPC number) or description of the target object to account for it in a transaction. 
     Another approach to identifying objects implements visual object recognition. Prior to recognizing a target object in a typical visual object recognition system, a database is built that includes visual features extracted from a plurality of known objects that one wants to recognize. To recognize the target object, sets of features are extracted from an image of the target object and the database is searched for the most similar feature to each feature in the set of extracted features (called a nearest neighbor). The nearest-neighbor matches from the database identify the known object to which the target object corresponds. 
     SUMMARY 
     In some embodiments, a method reduces a search space of a database including feature models used in visual object recognition. For example, the method includes obtaining an item-filter parameter corresponding to information encoded in a fragment of an optical code of an unknown item, in which the optical code fragment information is less than all the information encoded in a complete optical code; and determining a subset of feature models based on the item-filter parameter, wherein feature models of the subset correspond to a known item having known optical code information that includes the optical code fragment information encoded in the optical code of the unknown item. 
     In another embodiment, a method identifies an object having an optical code using a system that includes a database containing a set of feature models of known objects that have known optical code information associated therewith. For example, the method includes attempting to read the optical code; decoding a portion of the optical code; generating a filtered subset of feature models from the set of feature models based on the decoded portion of the optical code, each feature model of the filtered subset of feature models corresponding to a known object whose associated known optical code information includes a representation of the decoded portion of the optical code; capturing image data representing the object; extracting visual features from the image; and comparing the extracted visual features to the feature models of the filtered subset of feature models to identify a match between the object and a known object. 
     In some embodiments, an object identification system includes an optical code reader configured to attempt to decode a portion of the optical code; an image capture device configured to capture an image of the object; a feature detector unit configured to detect visual features in the image captured by the image capture device; a database configured to store feature models of a set of known objects that have known optical code information associated therewith; a database filter unit in communication with the optical code reader and the database, the database filter unit configured to, in response to the optical code reader decoding the portion of the optical code, receive a representation of the decoded portion of the optical code and generate a filtered subset of feature models based on the representation of the decoded portion of the optical code; and a comparison unit in communication with the feature detector unit and the database filter unit, the comparison unit configured to receive and compare the detected visual features and the feature models of the filtered subset of feature models to thereby identify a match between the object and a known object. 
     Additional aspects and advantages will be apparent from the following detailed description of embodiments, which proceeds with reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Understanding that drawings depict only certain embodiments and are not therefore to be considered to be limiting in nature, the embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings. 
         FIG. 1  is a block diagram of an object identification system according to one embodiment. 
         FIG. 2  is an isometric view of a checkstand, according to one embodiment, in which the object identification system of  FIG. 1  may be implemented. 
         FIG. 3  is a diagram of a flying-spot type scanner that may be used in the object identification system of  FIG. 1 . 
         FIG. 4  is a diagram of an imager-based data reader that may be used in the object identification system of  FIG. 1 . 
         FIG. 5  is a diagram of a database and its contents that may be used in the object identification system of  FIG. 1 . 
         FIG. 6  is an image of an example UPC type optical code. 
         FIG. 7  is a flowchart of one example method for identifying an object using the object identification system of  FIG. 1 . 
         FIG. 8  is a flowchart of one example method for reducing a search space of a database including feature models used in visual object recognition. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     With reference to the above-listed drawings, this section describes particular embodiments and their detailed construction and operation. The embodiments described herein are set forth by way of illustration only and not limitation. Skilled persons will recognize in light of the teachings herein that there is a range of equivalents to the example embodiments described herein. Most notably, other embodiments are possible, variations can be made to the embodiments described herein, and there may be equivalents to the components, parts, or steps that make up the described embodiments. 
     As skilled persons will appreciate in light of this disclosure, certain embodiments are capable of achieving certain advantages over the known prior art, including some or all of the following: (1) automatically resolving no-code exceptions, (2) increasing the speed and/or accuracy of object recognition systems, (3) utilizing decoded portions of non-decoded optical codes in a feed-forward manner to improve object recognition performance, and (4) enabling practical implementation of an object recognition system that is capable of recognizing a relatively large number of known objects. The present inventors have also recognized a difficulty with typical visual object recognition systems in that as the database increases in size (i.e., as the number of known objects desired to be recognized increases), it becomes increasingly difficult and inefficient to search through a search space of the entire database contents to find the nearest-neighbors. For example, in some applications it may be desirable to recognize thousands, tens of thousands, hundreds of thousands, or even millions of objects. These and other advantages of various embodiments will be apparent upon reading the remainder of this section. 
     For the sake of clarity and conciseness, certain aspects of components or steps of certain embodiments are presented without undue detail where such detail would be apparent to skilled persons in light of the teachings herein and/or where such detail would obfuscate an understanding of more pertinent aspects of the embodiments. 
       FIG. 1  is a block diagram of an object identification system  10  that is configured to identify an object  15  bearing an optical code  20 . The system  10  may be used in various different applications. In one example, the system  10  is used in a checkstand at a high-volume retail establishment such as a grocery store or big-box store. One suitable example of a checkstand is shown in  FIG. 2 , which shows an automated checkout system  25 . An example of an automated checkout system  25  is described in detail in U.S. application Ser. No. 13/357,356 and U.S. application Ser. No. 13/357,459. 
     As shown in  FIG. 1 , the system  10  includes an optical code reader  30  that includes a data acquisition subsystem  35  configured to scan (read) the optical code  20  and acquire data from it when the optical code  20  is in a read zone  37  of the optical code reader  30 . The optical code reader  30  may be any type of optical code reader including a manual reader, a semi-automatic reader, or an automated type reader. Furthermore, the optical code reader  30  may implement various types of data acquisition technologies. For example, the data acquisition subsystem  35  may correspond to a flying spot scanner  40  (e.g., a laser scanner) as shown in  FIG. 3 . In general, the flying spot scanner  40  includes (a) an illumination source  45  configured to produce an illumination beam  50  that is moved (e.g., scanned) across the optical code  20  and (b) a photodetector  55  that is configured to monitor reflected or backscattered light  60 . In another example shown in  FIG. 4 , the data acquisition subsystem  35  may correspond to an imager-based system  65  that employs an image capture device  70  (e.g., a camera) to capture an image of the optical code  20 . In general, the image capture device  70  includes an imager  75  (e.g., a 1D imager, a 2D imager) and light directing optics  80  to focus an image of the optical code  20  on the imager  75 . The image capture device  70  may use ambient light or may include a light source  85  to illuminate the optical code  20 . The imager  75  may be a monochrome imager or a color imager. Moreover, imager  75  may be any suitable imaging device such as, for example, a charge-coupled device (CCD) or a complementary metal oxide semiconductor (CMOS) imager. 
     The data acquisition subsystem  35  is configured to produce optical code data in response to reading the optical code  20 . The data acquisition subsystem  35  is further configured to communicate the optical code data to an optical code decoder unit  90  of the optical code reader  30 . The decoder unit  90  may reside on one or more processors, or may be embodied in one or more hardware constructs, that are housed within the optical code reader  30 . Alternatively, the decoder unit  90  may be housed remotely from the optical code reader  30  and may receive the optical code data over a communication network (e.g., a wired network, a wireless network). 
     The decoder unit  90  includes multiple decoders that correspond to different types of optical codes. Such a decoder unit is sometimes referred to as an auto-discriminating decoder. For example, the decoder unit  90  may include any suitable type of one-dimensional and higher-dimensional code decoder. The decoder unit  90  may be configured to decode one or more of the following symbology types: UPC, codabar, code 25, code 39, code 93, code 128, codell, EAN2, EAN13, plessy, POSTNET, aztec code, maxicode, PDF417, QR code, high-capacity color barcode, and data matrix, to name a few. It should be recognized that many other symbology types exist, and the decoder unit  90  may be configured to decode one or more of these other symbology types. 
     The system  10  also includes an object recognition subsystem  100  that is configured to recognize the object  15  based on its appearance characteristics or other characteristics. In one embodiment, the object recognition subsystem  100  includes an image capture device  105  (e.g., still picture camera, video camera) that is configured to capture one or more images of the object  15 . In one example, the image capture device  105  may be distinct and separate from the data acquisition subsystem  35  of the optical code reader  30 . In another example, the image capture device  105  may be one and the same as the image capture device  70  of the data acquisition subsystem  35  of the optical code reader  30 . In another example, image capture device  105  may be one of multiple image capture devices that are configured to capture images of the object  15  from different points of view to enable the object  15  to be recognized. The image capture device  105  may capture one or more images of the object  15  before or after the object  15  is in the read zone  37  of the optical code reader  30 . In another example, the image capture device  105  may capture one or more images of the object  15  when the object  15  is in the read zone  37  of the optical code reader  30 . 
     The image capture device  105  is configured to capture one or more images and produce image data that is communicated to a feature detector unit  110  of the object recognition system  100 . The feature detector unit  110  is configured to analyze the image data (e.g., via a feature detector algorithm) to detect and extract visual features that are used to recognize the object  15 . These detected visual features become part of a feature model of the object  15 . Various types of visual features may be used to recognize the object  15 . The visual features are geometric point features corresponding to points on the object  15  that are reliably detected and/or identified in an image of the object  15 . The geometric point features may correspond to image locations that satisfy specific properties. For example, the feature detector unit  110  may include a Harris Corner Detector that detects locations in an image where edge boundaries intersect. These intersections typically correspond to locations where the packaging graphics create corners on the object  15 . The term geometric point feature emphasizes that the features are defined at specific points in the image, and that the relative geometric relationship of features found in an image is useful for the object recognition process. The visual features of the object  15  may include a collection of information about the object  15  such as: an identifier to identify the object  15  or object model to which the feature belongs; the x and y position coordinates, scale and orientation of the visual feature; and a feature descriptor. 
     A feature descriptor (also referred to as descriptor, descriptor vector, feature vector, or local patch descriptor) is a quantified measure of some qualities of a detected feature used to identify and discriminate one feature from other features. Typically, the feature descriptor may take the form of a high-dimensional vector (feature vector) that is based on the pixel values of a patch of pixels around the feature location. Some feature descriptors are invariant to common image transformations, such as changes in scale, orientation, and illumination, so that the corresponding features of the object  15  observed in multiple images of the object  15  (that is, the same physical point on the object  15  detected in several images of the object  15  where image scale, orientation, and illumination vary) have similar (if not identical) feature descriptors. 
     Specific, non-limiting examples of features that may be used by object recognition system  100  include the following examples: scale-invariant feature transformation (SIFT) features described in U.S. Pat. No. 6,711,239; speeded up robust features (SURF), described in Herbert Bay et al., “SURF: Speeded Up Robust Features,” Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346-359 (2008); gradient location and orientation histogram (GLOH) features, described in Krystian Mikolajczyk &amp; Cordelia Schmid, “A performance evaluation of local descriptors,” IEEE Transactions on Pattern Analysis &amp; Machine Intelligence, No. 10, Vol. 27, pp. 1615-1630 (2005); DAISY features, described in Engin Tola et al., “DAISY: An Efficient Dense Descriptor Applied to Wide Baseline Stereo,” IEEE Transactions on Pattern Analysis and Machine Intelligence, (2009); and any other suitable features that encode the local appearance of the object  15 , such as, features that produce similar results irrespective of how the image of the object was captured (e.g., variations in illumination, scale, position and orientation). 
     The object recognition system  100  also includes a database  115  that is configured to store information associated with known objects that one wants to recognize.  FIG. 5  is a block diagram of the database  115  that shows representations of information associated with N known objects. The information may include a feature model for each of the N known objects. The feature models may be generated during a training process that is performed prior to attempting to recognize the object  15 . The feature models include the type of visual features detected by feature detector unit  110 . For example, the visual features of the known objects may include SIFT features, SURF, GLOH features, DAISY features, or any other suitable features that encode the local appearance of the known objects. The information associated with each known object also includes optical code information (e.g., the decoded number (e.g., UPC number), the optical code type (e.g., UPC, PDF417)) that corresponds to the known object. Other known object information, such as the known object&#39;s price, name, manufacturer, and product type, may also be stored in the database and associated with its corresponding known object. 
     The object recognition system  100  also includes a feature comparison unit  120  that is configured to receive the visual features of the object  15  that are identified by the feature detector unit  110 . The feature comparison unit  120  is also configured to receive the visual features of the known objects that are stored in the database  115  and to compare the feature model of the object  15  to the feature models of the known objects to identify the known object to which the object  15  corresponds. 
     As provided in U.S. patent application Ser. No. 13/107,824, titled “Systems and Methods for Object Recognition Using a Large Database,” which is hereby incorporated by reference in its entirety, the following is one non-limiting example of a process that the feature comparison unit  120  may use to recognize the object  15 . Each visual feature of the object  15  and the known objects may include a corresponding feature vector that is based on the pixel values of a patch of pixels around the feature location in an image. The feature comparison unit  120  may identify a nearest-neighbor for a visual feature of the object  15  by computing the Euclidean distances between the visual feature&#39;s feature vector and the feature vectors of the known objects. The known object feature vector corresponding to the shortest Euclidean distance corresponds to the nearest-neighbor of the particular visual feature of the object  15 . Once the nearest-neighbors in the database  115  are found for the visual features of the object  15 , the nearest-neighbors may be used by the feature comparison unit  120  to vote for the known objects associated with the nearest-neighbors. If multiple known objects are identified as candidate matches for the object  15 , the true known object match for the target object may be identified by determining which candidate match has the highest number of nearest-neighbor votes. One such known method of object recognition is described in U.S. Pat. No. 6,711,293, titled “Method and apparatus for identifying scale invariant features in an image and use of same for locating an object in an image.” It should be recognized that other measures of similarity between feature models may be used to recognize the object  15 . 
     The object recognition system  100  also includes a database filter unit  125  that is configured to receive data from the decoder unit  90  of the optical code reader  30 . The database filter unit  125  is configured to use the data from the decoder unit  90  to improve the performance of the object recognition system  100  by reducing the search space of database information. For example, the database filter unit  125  is configured to identify a filter parameter, based on the data received from the decoder unit  90 , that is used to obtain a subset of known objects that may correspond to (i.e., match) the object  15 , or a subset of known objects that likely do not correspond to the object  15 , as described in more detail below. 
     In some applications, the object recognition system  100  may be triggered in response to an unsuccessful decode of the optical code  20 . In that situation, data corresponding to an attempted decode may be transmitted to the database filter unit  125  so that the data may be used to facilitate efficient searching of the feature models of the known objects in the database  115 . In one embodiment, the data transmitted from the decoder unit  90  to database filter unit  125  correspond to partial decoded information that represents a decoded portion of the optical code  20 . For example, although the decoder unit  90  may be unable to fully decode the optical code  20 , the decoder unit  90  may be able to decode portions of the optical code  20  that convey information that may be useful in filtering out known object feature models in the object recognition process. 
     Various non-limiting examples of partial decoded information is described with reference to  FIG. 6 , which is a representation of a UPC code  130 . The UPC code  130  includes various portions that identify the code&#39;s type and/or convey certain information to a point of sale (POS) system. For example, the UPC code  130  includes start guard bars  135  (i.e., bar-space-bar or 101), middle guard bars  140  (i.e., space-bar-space-bar-space or 01010), and end guard bars  145  (i.e., bar-space-bar or 101) that may be used in conjunction with the quantity and widths of the bars and spaces in the characters  150 ,  155  and  160  to identify the code  130  as a UPC type code. The guard bars  135 ,  140 , and  145  also establish timing for reading the code. A typical UPC code  130  also includes a manufacturer identification number (a.k.a. a manufacturer code)  150  that identifies the manufacturer of the object  15 . The manufacturer identification number  150  in this example is “012345.” The first digit in the manufacturer identification number  150  may be a number system character  155  (a.k.a. a prefix) that keys the category (e.g., general items, random weight items such as produce, drug and health related items, coupons) related to the object  15 . The UPC code  130  may also include an item number  155  (a.k.a. a product code) that identifies the object bearing the UPC code  130 . The item number  155  in this example is “67890.” The UPC code  130  also includes a check digit  160 , which in this example is “5,” that is computed using the manufacturer identification number  150  and the item number  155 . The check digit  160  may be used to detect an error in the physical print of the UPC code  130  or in the reading of it. It should be understood that other code types may encode other types of information. 
     In an attempt to decode the UPC code  130 , the decoder unit  90  may decode one or more portions of the UPC code  130  without decoding it in its entirety. For example, the decoder unit  90  may be able to decode the start guard bars  135 , middle guard bars  140 , and/or the end guard bars  145  without decoding the other portions of the UPC code  130 . The decoder unit  90  may use the decoded guard bar portion(s)  135 ,  140 , and/or  145  to identify the code  130  as a UPC type code and communicate the code type to the database filter unit  125  to enable it to filter out the object models of known objects that do not have a corresponding UPC code type (e.g., filter out known objects that do not bear a UPC code). The database filter unit  125  may then retrieve only the feature models of known objects that have a corresponding UPC code type and supply those feature models to the comparison unit  120 . Accordingly, the database filter unit  125 , together with partial decoded data from the optical code reader  30 , may enable the object recognition system  100  to search through a subset, instead of the entire set, of the feature models in the database  115  for a match for the object  15 . In another example, the decoder unit  90  may be able to decode the manufacturer identification number  150  without decoding the item number  155 . In that situation, the decoder unit  90  may communicate the manufacturer identification number  150  to the database filter unit  125  to thereby enable it to retrieve only the feature models corresponding to known objects associated with the manufacturer identification number  150 . In another example, the decoder unit  90  may be able to decode the item number  155  without decoding the manufacturer identification number  150 . Accordingly, the decoder unit  90  may communicate the item number  155  to the database filter unit  125  to thereby enable it to retrieve only the feature models corresponding to known objects that have the item number  155 . In another example, the decoder unit  90  may be able to decode the check digit  160  without decoding the manufacturer identification number  150  and the item number  155 . The decoder unit  90  may communicate the check digit  160  to the database filter unit  125  to thereby enable it to retrieve only the feature models that have the corresponding check digit  160 . 
     There are several means that can be used for identifying data that matches partially decodable optical code data. For example, data stored in a database (such as the PostgreSQL database) may be selected using the Structured Query Language (SQL) by executing query commands that match inputs to database data entries using the SQL LIKE operator, the SQL-99 SIMILAR TO operator, regular expression matching, or other suitable operators. In one embodiment, for example, barcode data may be represented in the database as a string that is fifteen characters in length, including an L character to represent the leading guard pattern  135 , a C character to represent the center guard pattern  140 , a T character to represent the trailing guard pattern  145 , and the numeric characters 0 through 9 to represent the other encoded data and check characters  150 ,  155 , and  160 . Accordingly, a barcode reader may then read or scan a barcode fragment including a substring containing the digits 8905 and a trailing guard pattern T. Next, the SQL operation V LIKE ‘%8905T’ may then be used to locate a database value V for possible matches of the indentified information. In this example, the percent symbol matches zero or more characters of any alphanumeric character data. Consequently, this example will match any string in the database that ends with the string of characters 8905T. In another example, the barcode reader may find a UPC code that is substantially decodable except for several characters that may be undecodable due to various reasons (e.g., smudging, lighting conditions, or other reasons). The decodable information in this example may be represented by the string L01_45C678905T, where the underscore characters represent unknown (i.e., undecodable) wildcard characters in the barcode. A database query such as for example, SQL operation V LIKE ‘L01_45C678905T’ locates values V, in which the underscore characters match any alphanumeric characters in those fourth and fifth string positions and the other alphanumeric characters must be an exact match having corresponding string positions. The syntax used with a specific database implementation may differ from that shown in these examples, but the matching and filtering operations follow similar principles described above. 
       FIG. 7  is a flowchart of one method  170  that may be used in connection with the system  10  to identify the object  15 . The optical code reader  30  scans the optical code  20  to attempt to decode it and identify the object  15  (step  175 ). The decoder unit  90  decodes a portion of the optical code  20 , but not all of it (step  180 ). The optical code reader  30  communicates data representing the decoded portion of the optical code  20  to the database filter unit  125 , and the database filter unit  125  generates a filtered subset of feature models from the set of features stored in the database based on the decoded portion of the optical code  20  (step  185 ). For example, if the decoded portion of the optical code  20  corresponds to a check digit, the database filter unit  125  retrieves from the database  115  only those feature models that correspond to known objects having that check digit in their optical code information. The image capture device  105  of the object recognition system  100  (e.g., image capture device  70 , or a distinct unit as described above) captures an image of the object  15  (step  190 ). The feature detector unit  110  extracts visual features from the image captured by the image capture device  105  (step  195 ). The comparison unit  120  receives the extracted features from the feature detector unit  110  and the filtered subset of feature models from the database filter unit  125 . The comparison unit  120  compares the extracted features to the feature models of the filtered subset of feature models to find one or more matches (step  200 ). 
     In response to the decoder unit  90  decoding only the portion of the optical code  20  at step  180 , the image capture device  105  may be triggered to capture the image at step  190  and the feature detector unit  110  may be triggered to extract the visual features from the image. In some embodiments, the image capture device  105  may capture the image at step  190  (and the visual features may be extracted from the image) before or during scanning of the optical code  20  with the optical code reader  30 . Accordingly, operation of the image capture device  105  and the feature detector unit  110  need not be triggered by the decoding outcome of the optical code reader  30 . Thus, in some embodiments, the image from step  190  may also be used for reading the optical code at step  175 , while the feature detector unit  110  may extract visual features from the same image. 
       FIG. 8  is a flowchart of one method  220  that may be used in connection with the system  10  to reduce a search space of the database  115  including feature models used in visual object recognition. Step  225  includes obtaining an item-filter parameter corresponding to incomplete information (e.g., optical code fragment information) encoded in an optical code of an unknown item. For example, the item-filter parameter may be the decoded portion of the optical code  20  that corresponds the check digit  160  ( FIG. 5 ) or other types of incomplete information encoded in the optical code  20  of the unknown item  15 . Step  235  includes determining a subset of feature models based on the item-filter parameter, wherein feature models of the subset correspond to a known item having known optical code information that includes the incomplete information encoded in the optical code  20 , for example. In one embodiment, the database filter unit  125  reduces the database search space by querying and obtaining from the database  115  only those feature models that correspond to known objects having a check digit value in their optical code information that is the same as that of the item-filter parameter. 
     As used herein, the term unit is a component that may comprise one or more hardware circuits or devices and/or one or more software routines, functions, object or the like. A unit may be entirely hardware, entirely software, comprise firmware, or comprise some combination of the foregoing. As used herein, the term system refers to a tangible object or group of objects, or a combination of functional components. 
     The methods, units, and systems illustrated and described herein can exist in a variety of forms both active and inactive. For example, they can exist partly or wholly as one or more software programs comprised of program instructions in source code, object code, executable code or other formats. Any of the above can be embodied in compressed or uncompressed form on computer-readable medium. 
     The terms and descriptions used above are set forth by way of illustration only and are not meant as limitations. It will be understood by skilled persons that many changes may be made to the details of the above-described embodiments without departing from the underlying principles of the invention. The scope of the present invention should, therefore, be determined only by the following claims.