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WO2003001435A1 - Identification d'objets sur la base d'images - Google Patents

Identification d'objets sur la base d'images Download PDF

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Publication number
WO2003001435A1
WO2003001435A1 PCT/IB2002/003352 IB0203352W WO03001435A1 WO 2003001435 A1 WO2003001435 A1 WO 2003001435A1 IB 0203352 W IB0203352 W IB 0203352W WO 03001435 A1 WO03001435 A1 WO 03001435A1
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WO
WIPO (PCT)
Prior art keywords
image
barcode
information
algorithms
user
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/IB2002/003352
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English (en)
Inventor
Tvsi Lev
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Emblaze Systems Ltd
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Emblaze Systems Ltd
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Publication of WO2003001435A1 publication Critical patent/WO2003001435A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/00127—Connection or combination of a still picture apparatus with another apparatus, e.g. for storage, processing or transmission of still picture signals or of information associated with a still picture
    • H04N1/00326—Connection or combination of a still picture apparatus with another apparatus, e.g. for storage, processing or transmission of still picture signals or of information associated with a still picture with a data reading, recognizing or recording apparatus, e.g. with a bar-code apparatus
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
    • G06F16/43—Querying
    • G06F16/432—Query formulation
    • G06F16/434—Query formulation using image data, e.g. images, photos, pictures taken by a user
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
    • G07G1/00—Cash registers
    • G07G1/0036—Checkout procedures
    • G07G1/0045—Checkout procedures with a code reader for reading of an identifying code of the article to be registered, e.g. barcode reader or radio-frequency identity [RFID] reader
    • G07G1/0054—Checkout procedures with a code reader for reading of an identifying code of the article to be registered, e.g. barcode reader or radio-frequency identity [RFID] reader with control of supplementary check-parameters, e.g. weight or number of articles
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/00127—Connection or combination of a still picture apparatus with another apparatus, e.g. for storage, processing or transmission of still picture signals or of information associated with a still picture
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/00127—Connection or combination of a still picture apparatus with another apparatus, e.g. for storage, processing or transmission of still picture signals or of information associated with a still picture
    • H04N1/00204—Connection or combination of a still picture apparatus with another apparatus, e.g. for storage, processing or transmission of still picture signals or of information associated with a still picture with a digital computer or a digital computer system, e.g. an internet server
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/00127—Connection or combination of a still picture apparatus with another apparatus, e.g. for storage, processing or transmission of still picture signals or of information associated with a still picture
    • H04N1/00281—Connection or combination of a still picture apparatus with another apparatus, e.g. for storage, processing or transmission of still picture signals or of information associated with a still picture with a telecommunication apparatus, e.g. a switched network of teleprinters for the distribution of text-based information, a selective call terminal
    • H04N1/00307—Connection or combination of a still picture apparatus with another apparatus, e.g. for storage, processing or transmission of still picture signals or of information associated with a still picture with a telecommunication apparatus, e.g. a switched network of teleprinters for the distribution of text-based information, a selective call terminal with a mobile telephone apparatus
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N2101/00—Still video cameras

Definitions

  • a visual system and method for object identification that enables users utilizing an imaging device to obtain information about, select, purchase, or perform other operation on objects.
  • the use may be for commerce, but is not limited to the context of commerce.
  • the object will be identified by the image or image sequence captured with the imaging device.
  • the present invention can replace the existing methods gaining information, for purchasing, and/or for making payments, or may combine two or all of these methods, all based on the visual identification of objects and algorithms for processing information related to such visual identification.
  • barcode scanners are attached to a computer, PDA, cellular phone, or some such similar user device.
  • the user scans the desired product with a barcode scanner, then the product code is extracted and used to identify the product for performing a commerce related operation such as buying the product.
  • a buyer marks on a catalog, or a special form, which items and what quantities he/she wants to order, and sends a fax of the document as an order form.
  • the fax may be embedded as an attached file in an email to the seller of the item.
  • Cellular phones and wireless PDA's can be used for performing payments with the proper e-wallet software for transferring the credit card number, authenticating the user and verifying his password and/or performing biometric tests.
  • Payments using a cellular phone or PDA can be performed by having the unit communicate wirelessly using IR, Bluetooth, acoustic signals or cellular network wireless protocols such as GSM, CDMA, etc.
  • the checkout unit must then include a communication device that increases costs for the retailer and requires installation.
  • payments or product selection using a cellular phone or PDA can be performed by having the user enter into the device a phone number, a web address or some other access code which is marked on the checkout unit (and/or the product identification code) and then establish a wireless connection to a remote payment management unit.
  • the disadvantage of this method is that the user has to enter a relatively long number/code by typing, speaking or writing (e.g., with a stylus), a process that is cumbersome and error prone.
  • [16] An imaging device, capable of capturing one-dimensional or two- dimensional images of objects.
  • [17] A device capable of sending the coded image through a wired/wireless channel to remote facilities.
  • the present invention relates generally to image based object identification, and more specifically to a visual method for object identification.
  • the invention enables users that utilize an imaging device to obtain information about, select, purchase, or perform other operation on objects. Each object will be identified by the image or image sequence captured with the imaging device.
  • Typical activities enabled by the invention are: [23] 1. Selecting an object for inquiring more information about it, e.g., requesting an independent review of a book one encounters in a book-store, medical information about a drug, etc.
  • Information read by user device (which may be a mobile or fixed handheld unit, a personal computer or some such other device) will then be used to determine the type of service or product to be purchased, and the payment required.
  • [28] 6 Selecting a reference for a physical object (e.g., a picture of the object, its name, its product code, or an advertisement of the product) and then performing any or all of the operations detailed in 1-5. For example, the user may see an item of food, then price, order, and pay for the food, all based on the visual identification of the object and the algorithms associated with the invention.
  • a reference for a physical object e.g., a picture of the object, its name, its product code, or an advertisement of the product
  • the present invention solves all of these problems by enabling users utilizing an imaging device to obtain information about, select, purchase, or perform other operations on objects/products in the context of commerce or in non-commercial contexts.
  • the object will be identified by the image or image sequence captured with the imaging device.
  • a credit card number transmitting can be based on the identification of the wireless device owner.
  • the visual method for object identification in commerce substantially departs from the conventional concepts and designs of the prior art, and in so doing provides an apparatus primarily developed to enable users utilizing an imaging device to obtain information about, select, purchase, or perform other operation on objects in the context of commerce or in non-commercial contexts.
  • the object will be identified by the image or image sequence captured with the imaging device.
  • Figure 1 illustrates an aspect of the invention. The selection of an object for the purpose of inquiring more information about it.
  • a person is in a specific store (which may be physical or virtual).
  • 1.2 The person takes an image of a specific book with a mobile phone
  • FIG. 1 illustrates another aspect of the invention. The selection of an object for the purpose of adding it to a virtual "shopping cart" - thus allowing the user to go through a store, quickly adding products to such a cart and then ordering them on the spot or later when reviewing the total order made.
  • a person is in a store, for example, a grocery store.
  • FIG. 4 illustrates another aspect of the invention. The selection of an object from a catalog, for the purpose of making a purchase order. [48] 3.1 A person looks at a fashion catalog [49] 3.2 A product is selected and photographed directly from the catalog using a cellular telephone, PDA or any other device capable of taking a picture. [50] 3.3 The product is added to the shopping list
  • Figure 4 illustrates another aspect of the invention. Performing a payment operation by pointing at a properly marked cash register/check out counter/label.
  • [53] 4.1 A person is in a parking lot.
  • [54] 4.2 The person, in this case perhaps a parking lot attendant, points at a plate containing information about the payment action required and takes a picture of the plate.
  • [55] 4.3 The payment is performed and the parking lot's gate is open (It will be appreciated, however, that this method can be totally automated, without any human involvement.)
  • Figure 5 illustrates another aspect of the invention. Uploading a coupon or other evidence of a discount, to be used later when purchasing a specific item.
  • the serial number of the requested product in a catalog, the digits of a barcode, a phone number for customer services etc.) is extracted from the entire image and is compressed.
  • the image is transmitted to and received by a base station.
  • the image information and the identification of the user is transferred from the base station to UcnGO's servers, or other server, through an IP net, or other digital network.
  • the image is processed in the server.
  • the relevant text/digits/icons/watermarks etc. are identified using OCR (optical character recognition) or ICR (intelligent character recognition).
  • the required service is performed. For example: the cell operator, another UCnGO server or a third party performs a payment operation.
  • Figure 7 illustrates the cross correlation map.
  • Figure 8 illustrates the binarized cross correlation map.
  • Figure 9 illustrates the center of mass
  • Figure 10 illustrates how the barcode candidate is rotated to become a horizontal output image.
  • Figure 11 illustrates results of cross correlation.
  • Figure 12 shows the rotated barcode.
  • Figure 13 shows the translated barcode.
  • Figure 14 shows the input image.
  • Figure 15 shows the result of NCC.
  • Figure 16 shows the output image.
  • Figure 17 shows the graph of the correlation of the barcode when a template is chosen.
  • Figure 18 illustrates the long while lines indicating where the barcode ends.
  • Figure 19 shows the template matching result.
  • FIG. 20 shows the location of the bottom of the digits.
  • Figure 21 shows the input image.
  • Figure 22 shows the corrected image.
  • Figure 23 shows the image that is used to cut the barcode in X.
  • Figure 24 illustrates the finding of the barcode width used for cutting.
  • Figure 25 shows a raw image.
  • Figure 26 shows after homogenization process.
  • Figure 27 shows an NN initial detection.
  • Figure 28 shows a page scan application.
  • Figure 29 shows Note Messaging application.
  • Figure 30 shows Buying from a Catalog application.
  • Figure 31 shows Snap and Share application.
  • Figure 32 shows a paper Portal application.
  • Figure 33 shows an Image Processing Server.
  • Figure 34 shows an example System Architecture.
  • FIG 35 - 63 show various stages in the implementation of the barcode example discussed herein.
  • [ 100] An imaging device, capable of taking one-dimensional or two- dimensional images of objects.
  • a device capable of sending the coded image through a wired/wireless channel to remote facilities.
  • the imaging device captures images or video sequences, which may be processed on this device, or may be transmitted to another device for processing.
  • the processed data is then transmitted and transferred through some kind of data network or networks to servers which process the information using the above-described algorithms, and then uses the extracted information for various applications.
  • the servers (or other connected entities) may then send information back through the network to the wireless device, or to other devices such as a personal computer or set-top box.
  • the identification of the imaged object and additional information such as the user's location, preferences and/or input are used to assist in determining the operation performed (e.g., if the object is a cash register the operation is payment, if the object is a can of juice and the user is in a supermarket the operation is add to shopping cart, if the user chose the "information only" option then the operation is to send information about the imaged object back to the computer/portable device etc.) or the operation menu offered to the user.
  • a large portion of the processing algorithms may reside on the portable device, and there may be a dynamically changing division of the algorithms running on the different parts of the system based on relative computational loads and desired user response times, changing imaging and wireless bandwidth conditions.
  • the application software executing for a given image or image sequence may be determined based on the image content itself, rather than being fixed. The user may choose the application software based on pre-configured parameters or during the operation.
  • the principle of operation is that using images or video sequences, a computer can decode the identity of the imaged object, for example a labeled product, a printed form, a page from a book or newspaper, a bill, a membership card, a receipt, a business card, a medical prescription etc.
  • This saves the user the time and effort of inputting the object identity and/or unique information pertaining to the object such as values in numerical fields, addresses in a business card, etc.
  • This also facilitates "one click" like commerce operations on physical objects in the real world, in the sense that the user is not required to repeat the imaging capture process.
  • the imaging device captures images or video sequences, which may be processed on this device, or processed by another device, and then transmitted and transferred through some kind of data network or networks to servers.
  • the servers process the information using the above-described algorithms, and then use the extracted information for various applications.
  • the servers (or other connected entities) may then send information back through the network to the wireless device, or to other devices such as a personal computer or set-top box.
  • the imaging device is a unit capable of acquiring images, storing and/or sending them.
  • the imaging device is a device capable of capturing single or multiple images or video streams and converting them to digital information. It is equipped with the proper optical and electro-optical imaging components and with computational and data storage.
  • the imaging device can be a digital camera, a PDA with an internal or external camera, a cellular phone with an internal or external camera, or a portable computational device (e.g., laptop, palmtop or web pad-like device with an internal or external camera).
  • the transmitting device is capable of sending images to remote facilities.
  • Such device may be a cellular phone, PDA, or other wireless device, but may also be a wired communication device.
  • the transmitting device is a device capable of transferring information to remote or nearby locations. It is capable of getting the information from the imaging device for processing and transmission. It is capable of receiving information wirelessly or using a wired connection.
  • the transmitting device can be a cellular phone, a wireless PDA, a web pad-like device communicating on a local wireless area network, a device communicating using infrared or acoustic energy, etc.
  • the image processing algorithms perform compression, artifact correction, noise reduction, color corrections, geometric corrections, imager non- uniformity correction, etc., and various image processing enhancement operations to better facilitate the operation of the next stage of image understanding algorithms. It is implemented as a plurality of software objects residing on one or more computational devices.
  • the image processing algorithms are numerical and symbolic algorithms for the manipulation of images and video streams.
  • the algorithms can be implemented as software running on a general purpose processor, DSP processor, special purpose ASIC and/or FGPA's. They can be a mixture of custom developed algorithms and libraries provided by other developers or companies. They can be arranged in any logical sequence, with potential changes in the sequence of processing or parameters governing the processing determined by image type, computational requirements or outputs from other algorithms.
  • the machine vision algorithms perform, among other operations, digit recognition, printed and handwritten text recognition, symbol, logo and watermark recognition, and general shape recognition.
  • the image processing algorithms are numerical and symbolic algorithms for the manipulation of images and video streams.
  • the algorithms may reside on a different system belonging to a different entity than the image processing algorithms or the application software.
  • the system also embodies software for utilizing the information extracted in the previous computation stages for data storage, extraction and/or communication with a plurality of internal and/or external applications, such as databases, search engines, price comparison sites etc.
  • the application software provides the overall functionality of the service, based on the information extracted in the previous algorithmic stages. It is software for data storage, extraction and/or communication with a plurality of internal and/or external applications, such as databases, search engines, price comparison sites etc.
  • the application software can be implemented as code running on a general purpose-processor, DSP processor, special purpose ASIC and/or FGPA's. It can be a mixture of custom developed software and libraries provided by other developers or companies. This software may reside on a different system belonging to a different entity than the rest of the system.
  • Color ID 1) Adopt Illumination Model. (This algorithm, which is new, is the use of existing lighting algorithms to estimate the spectral distribution of the illumination source in the image); 2) Project Image to Base Image Color Space. (This algorithm, which is new, is the transformation of RGB coordinates of each pixel in the image to create a new image, representing the objects in the original image as they would appear under some standard reference illumination); 3) Match Color + Correlation Matching. (This algorithm is also new. Normally, cross-correlation methods work on gray level images.
  • the novelty here is that one can perform cross-co ⁇ elation where the normal scalar dot product between pixels in the template and the target image is replaced by a different mathematical operator, e.g., a scalar dot product of R, G, and B coordinates of the two pixels in the template and target image.
  • Connect Things AB was launched in 1999 and later acquired and renamed "AirClic".
  • the company develops a laser barcode reader designed to be connected to keyboards and mobile phones. Mobile phones equipped with this device can scan barcode and supply the user with the WAP page relevant to the product being scanned.
  • TicketsAnywhere was founded in 2000 as a spin-off from Netlight Consulting AB.
  • the company offers a platform for mobile ticket, vouchers, and coupons. This service allows the user to book and buy tickets via a cell phone.
  • Wireless ticket booking is an excellent example of how easily mobile applications can be used for commerce.
  • the algorithm consists of 6 main steps (that will be described in details in the following paragraph): [140] 1. Identify the barcode in the image, by recognizing regions in the image that resemble barcodes (uniformity in one axis and change in the other, etc.) regardless of the image rotation, the tilt of the image plane to the camera and the scale (to a reasonable extent). [141] 2. Based on the above identification, recognize the dimensions, orientation and location of the barcode. [142] 3. Extract a normalized image strip of the digits accompanying the barcode and correct the image removing any geometric distortions caused by camera pose of internal camera attributes. [143] 4.
  • This function searches to find potential barcode areas in the image. It then evaluates the angle by which the barcode is rotated and rotates the image by that angle. This function also translates the rotated barcode image, so that it coincides with the image center. The detected barcode is then tested to determine the likelihood that positive product identification can be made from this image. The entire process is repeated in video frame rate, allowing the algorithm to choose the frame most suitable for the detection of the barcode digits.
  • the image is divided into square regions, 32x32 pixels in size.
  • a template is created by taking an 8x8 pixel array from the center of the region.
  • a correlation map is formed. This map is bright for those regions where the template was found. Due to the barcode property of consisting of a series of parallel lines, the co ⁇ elation map assumes a very specific shape for those areas which are barcode.
  • the correlation map looks like a very bright line over a dark background.
  • the angle in which the line a slanted is the angle in which the barcode lines appear in the image. Regions that are part of the barcode result with long narrow white lines, while other regions may have a small bright spot in the region center or have a large bright/dark areas that are not narrow and long.
  • Figure 7 illustrates a correlation map of a typical barcode image.
  • each block is then determined by detecting the left and right edges of the barcode candidate block. This data will later be used in order to rescale the image, as is required by the digit recognition algorithms.
  • three round dots are placed over the center of mass, as well as the right and left edges of the block, (for demonstration purposes only) (3) Rotating the barcode
  • This function computes the barcode's rotation angle with a maximal error of 1 degree by performing a normalized cross correlation operation.
  • the template is a 32x32 block, taken from the best barcode candidate centroid.
  • Figure 11 shows the result of this cross correlation.
  • the angle of this line is calculated by a least mean square approximation to a line. The best line fit is found, and its angle is computed.
  • This function finds the barcode height and digit size. This data is required in order to rescale the barcode to a proper scale, as required by the OCR algorithms.
  • the barcode digit size is found by detecting the lower edge of the barcode lines as the upper digit delimiter , and the detecting the white strip that is under the digits, as a lower digit delimiter.
  • the barcode's lower edge is detected by finding the cross correlation image between the barcode itself, using a wide template taken from the upper barcode part, 1x64 pixels in size.
  • Figure 14 and Figure 15 show the raw barcode image and the result of this cross correlation, respectively. This correlation is very high inside the barcode area due to the nature of the barcode being a series of parallel lines.
  • the resulting image is also cut from above and below the digits, using the data that was previously measured, of the barcode bottom edge and the digit lower delimiter.
  • the resulting image can be seen in Figure 25.
  • Figs. 28-33 is entitled, "Page Scan.”
  • the user captures one or more images of parts or of the whole of a printed document.
  • the images are uploaded to the UCnGo server, where the images are first enhanced, and then stitched to form one large image.
  • This large image can then be formatted for display and printing in various devices, such as fax machines, e-mail attachments, graphical file formats, etc.
  • Fig. 29 is entitled, "Note Messaging.”
  • the user captures one or more images of parts or the whole of some handwritten note or page.
  • the images are uploaded to the UCnGo server, where the images are first enhanced, and then stitched to form one large image.
  • This large image is then processed with special enhancements developed to make handwritten text (regardless of the color of the ink or the color of the background) more legible in the different display and print formats.
  • This large image can then be formatted for display and printing in various devices, such as fax machines, e- mail attachments, graphical file formats, etc.
  • Fig. 30 is entitled, "Buy from Catalog.”
  • the user captures one or more images of parts or of the whole of some product.
  • the images are uploaded to the UCnGo server, where the images are first enhanced and then stitched to form one large image.
  • This large image is processed to locate special marks or signs, and then to perform OCR on numerals or letters identified by these special marks (e.g., note the bull's-eye marks in figure). These numerals or letters are then used to search in a database and to identify uniquely a product that the user wants to add to his or her shopping list.
  • Fig. 31 is entitled, "Snap n Share.”
  • This application is in essence similar to the application “Page Scan” described above, except that here the image to be captured is a note on a billboard, a sign, or some other written communication other than a page.
  • Fig. 32 is entitled "Paper Portal.”
  • the user captures one or more images of parts or of the whole of some newspaper or magazine.
  • the images are uploaded to the UCnGo server, where the images are first enhanced, and then stitched to form one large image.
  • This large image is then processed to locate headlines, special symbols, etc., which typically appear in a magazine or a newspaper.
  • the OCR is performed on these particular sections of the large image.
  • the decoded text is then used to search in a database and to identify uniquely a news story, an advertisement, ct., about which the user wants to receive additional information.
  • Fig. 33 is entitled, "UCnGo Image Processing Server,” and shows a shopping application, in this particular case the purchase of a CD.
  • the user captures one or more images of parts or of the whole of some product.
  • the images are uploaded to the UCnGo server, where the images are first enhanced and then stitched to form one large image.
  • This large image is then processed to locate special marks, barcodes, text, or logos. Barcode decoding and/or OCR are then performed on numerals or letters.
  • the numerals, codes, logos, and/or text are then used to search in a database and to identify a product that the user wanted to add to his or her shopping list, or about which the user wanted to perform comparative shopping, or for some other reason requiring more information about the product.
  • Fig. 34 is entitled “System Architecture.” Of particular note is the box at the middle left, named “UcnGo Image Server” on the sheet. This box shows the static elements of the applicant's system, marked as “UcnGo System,” including the Image Processing Server, the Application Server, the Web site intermediary, and the Billing Server. Each static element is connected to external elements by the lines indicated on the sheet. Some of the protocols by which communication is effected are also listed on the sheet.
  • Image Processing Server This receives digital imaging information for processing in accordance with the algorithms described herein.
  • Application Server This conducts load balancing and system management in accordance with a rule-based system, all as indicated on the sheet.
  • the Web Site Intermediary This connects to the Internet or other data network.
  • Billing Server This connects to billing clients, which typically will have databases with information necessary to management the billing process.
  • Adjust image [179] This part takes care for the light nonuniformity of the image.
  • the input image is expected to be in uint ⁇ format.
  • the size of it is (240,320).
  • the barcode in the input image is expected to be centered and touching the upper side (approximately 40 upper pixels of the image should be the barcode area) of the image.
  • N_middle_strip thinner than the middle strip. This is because sometimes the barcode is not exactly centered. We are looking now to the lower edge of the digits. Actually we are looking at the white line under the digits. The method we use is sensitive to cases when we do not include in the image additional patterns besides part of barcode and the digits, that's why we take the strip thinner .
  • the N_middle_strip is shown in Figure 45.
  • Input This section of the code takes a binarized image of the barcode area, where the barcode has already been straightened (that is the barcode Lines are parallel to the Y axis in the Matlab axis convention). Furthermore, the Y cut which identifies the upper edge of the digits has already been performed, and this Y cut line is roughly at the center of The binarized input image.
  • Averaged_Image is the result of doing a blurring operation for a window as shown in Figure 54. The size of 3 pixels (height or Y) by 15 pixels (width or X).
  • averaged_strip is the result of doing a blurring operation for a window of the size of 5 pixels (height or Y) by 2 pixels (width or X). As a result of averaging some bottom lines of the averaged_strip become uniformly grey. We define the number of these lines by grey lines and ignore them later as shown in Figure 57.
  • the parameters to variate are: [266] 1. The slope of the line connecting all the points. [267] 2. The bias of the above line.
  • the model can be adapted for curved surfaces. This is done by adding additional variable parameter of curvature of the line with the points. Also the problem of the perspective can be solved by permitting the distance between the points to be changing by a constant factor (as we move away from the center) and to vary this factor.

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  • Image Processing (AREA)

Abstract

L'invention concerne un système d'identification d'objets qui permet à l'utilisateur exploitant un dispositif d'imagerie d'obtenir des informations sur des objets, de choisir et d'acheter lesdits objets, ou d'effectuer d'autres opérations sur des objets. Le système comporte un dispositif d'imagerie permettant de capturer des images d'objets unidimensionnelles ou bidimensionnelles. Un dispositif permet d'envoyer l'image codée par le biais d'un canal câblé/sans fil à des installations à distance. Des algorithmes et logiciels permettent de traiter et d'analyser les images, et d'en extraire des informations symboliques telles que chiffres, lettres, textes, logos, symboles ou icônes. L'invention concerne en outre des algorithmes et logiciels qui facilitent l'identification des objets imagés sur la base des informations recueillies de l'image et des informations disponibles dans des bases de données. De plus, elle concerne des algorithmes et logiciels qui offrent à l'utilisateur du dispositif d'imagerie divers informations et services fondés sur les informations recueillies à partir de l'image et sur celles disponibles dans des bases de données.
PCT/IB2002/003352 2001-06-22 2002-06-21 Identification d'objets sur la base d'images Ceased WO2003001435A1 (fr)

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US29973401P 2001-06-22 2001-06-22
US60/299,734 2001-06-22

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WO2016197219A1 (fr) * 2015-06-10 2016-12-15 Valid Soluções E Serviços De Segurança Em Meios De Pagamento E Identificação S.A. Procédé et système d'identification de produits en mouvement dans une chaîne de production
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EP3816920A1 (fr) * 2019-10-31 2021-05-05 The Goodyear Tire & Rubber Company Procédé d'obtention et de traitement d'informations sur des pneus

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US8292180B2 (en) 2001-07-13 2012-10-23 Hand Held Products, Inc. Optical reader having an imager
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US7220562B2 (en) 2003-05-08 2007-05-22 E. I. Du Pont De Nemours And Company Preparation of (E)- and (Z)-2-methyl-2-butenoic acids
EP1503325A1 (fr) * 2003-08-01 2005-02-02 The Secretary of State acting through Ordnance Survey Symboles intelligents
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US7565139B2 (en) 2004-02-20 2009-07-21 Google Inc. Image-based search engine for mobile phones with camera
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WO2006025797A1 (fr) * 2004-09-01 2006-03-09 Creative Technology Ltd Systeme de recherche
EP1817902A4 (fr) * 2004-11-15 2008-11-19 Agere Systems Inc Numeriseur de documents integre dans un telephone cellulaire
CN1332980C (zh) * 2004-12-10 2007-08-22 中国科学院上海生命科学研究院 水稻抗逆相关基因－锚定序列重复蛋白基因及其应用
DE102004061171A1 (de) * 2004-12-16 2006-06-29 Vodafone Holding Gmbh Anregung und/oder Steigerung eines Erwerbs von Produkten und/oder einer Inanspruchnahme von Dienstleistungen
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US11323650B2 (en) 2005-03-11 2022-05-03 Hand Held Products, Inc. Image reader comprising CMOS based image sensor array
US7611060B2 (en) 2005-03-11 2009-11-03 Hand Held Products, Inc. System and method to automatically focus an image reader
US7568628B2 (en) 2005-03-11 2009-08-04 Hand Held Products, Inc. Bar code reading device with global electronic shutter control
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US10002272B2 (en) 2005-06-03 2018-06-19 Hand Held Products, Inc. Apparatus having hybrid monochrome and color image sensor array
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US10691907B2 (en) 2005-06-03 2020-06-23 Hand Held Products, Inc. Apparatus having hybrid monochrome and color image sensor array
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US10949634B2 (en) 2005-06-03 2021-03-16 Hand Held Products, Inc. Apparatus having hybrid monochrome and color image sensor array
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US11625550B2 (en) 2005-06-03 2023-04-11 Hand Held Products, Inc. Apparatus having hybrid monochrome and color image sensor array
US9092654B2 (en) 2005-06-03 2015-07-28 Hand Held Products, Inc. Digital picture taking optical reader having hybrid monochrome and color image sensor array
US11238252B2 (en) 2005-06-03 2022-02-01 Hand Held Products, Inc. Apparatus having hybrid monochrome and color image sensor array
US7780089B2 (en) 2005-06-03 2010-08-24 Hand Held Products, Inc. Digital picture taking optical reader having hybrid monochrome and color image sensor array
US11604933B2 (en) 2005-06-03 2023-03-14 Hand Held Products, Inc. Apparatus having hybrid monochrome and color image sensor array
WO2007048823A1 (fr) * 2005-10-27 2007-05-03 Sony Ericsson Mobile Communications Ab Configuration d'un dispositif electronique
US7558950B2 (en) 2005-10-27 2009-07-07 Sony Ericsson Mobile Communications Ab Methods of configuring an electronic device to be operable with an electronic apparatus based on automatic identification thereof and related devices
US7917286B2 (en) 2005-12-16 2011-03-29 Google Inc. Database assisted OCR for street scenes and other images
EP1998283A4 (fr) * 2006-03-23 2010-01-27 Olympus Corp Appareil et terminal de presentation d'informations
DE102006050409A1 (de) * 2006-06-02 2007-12-06 Gavitec Ag System und Verfahren zum Bild- und Datenupload mit mobilem Endgerät
US8355533B2 (en) 2007-03-05 2013-01-15 Superfish Ltd. Method for providing photographed image-related information to user, and mobile system therefor
WO2008107876A1 (fr) * 2007-03-05 2008-09-12 Link It Ltd. Procédé et système mobile permettant de fournir à un utilisateur de mobile une information associée à une image photographiée
US8190195B2 (en) 2008-03-03 2012-05-29 Linguatec Sprachtechnologien Gmbh System and method for data correlation and mobile terminal therefor
WO2009112398A3 (fr) * 2008-03-03 2009-11-05 Linguatec Sprachtechnologien Gmbh Système et procédé de corrélation de données et terminal destiné à cet effet
US8624989B2 (en) 2008-07-01 2014-01-07 Sony Corporation System and method for remotely performing image processing operations with a network server device
WO2013191598A1 (fr) * 2012-06-18 2013-12-27 Sca Hygiene Products Ab Procédé de fourniture d'informations associées à un produit
WO2016197219A1 (fr) * 2015-06-10 2016-12-15 Valid Soluções E Serviços De Segurança Em Meios De Pagamento E Identificação S.A. Procédé et système d'identification de produits en mouvement dans une chaîne de production
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US10725630B2 (en) * 2016-01-04 2020-07-28 Lenovo (Singapore) Pte Ltd Selecting a target application based on content
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EP3816920A1 (fr) * 2019-10-31 2021-05-05 The Goodyear Tire & Rubber Company Procédé d'obtention et de traitement d'informations sur des pneus

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