AU740004B3 - Item recognition method and apparatus - Google Patents

Description

P/00/012 Regulation 3.2

AUSTRALIA

Patents Act 1990

ORIGINAL

COMPLETE SPECIFICATION PETTY PATENT Invention Title: ITEM RECOGNITION

APPARATUS

METHOD AND The following statement is a full description of this invention, including the best method of performing it known to me: 1 W:-JOHN\RN644127.do 2 ITEM RECOGNITION METHOD AND APPARATUS The present application is a divisional application of Australian Patent Application 64155/00, the contents of which are hereby incorporated herein by reference. The present invention relates to a method and apparatus for item recognition and, more specifically, to the automatic recognition of items at a "point of sale" (POS) in a retail outlet, such as at a hardware store checkout.

In all retail environments the various items of purchase need to be identified for entry into a computerised inventory and register system. This is very often done by applying a UPC (or similar) barcode to the item and then using the scanner at the POS to scan the barcode in order to produce a unique data entry for passing to the computerised system. This system is popular and effective and used now in all types of retail outlets. However, in the case of some items this approach is simply unrealistic, as applying a bar code to the item can be impractical. For example, in a hardware store a shopper may be purchasing small, loose items such as nuts, bolts and other fasteners. It may not be cost-effective or it may not be possible or convenient to reliably apply a bar code to such items, and the present approach to addressing this problem is to leave the job of item identification to the checkout operators, each checkout being provided with a manual look-up directory whose pages display images of each item sorted by category and presented alongside an individual barcode. The operator will look up the item image in the relevant category and, once it has been identified, will simply scan the accompanying barcode to enter the item into the computerised system. Whilst this system is generally fairly reliable it is naturally timeconsuming and inefficient, and relies on an accurate categorisation of the various items and continuous updates to the manual directories. Thought has been given to computerising this procedure, and displaying the directory on a screen for enquiry by the checkout operator, but this approach does little to relieve the basic inefficiency of such an identification procedure at the POS.

According to the present invention, there is provided item recognition apparatus for recognising an individual item from a variety of known products for purposes of entry into an inventory and/or register system, the apparatus including: an identification station having: a viewplate with backside illumination to provide a backlit image viewable from the frontside of the viewplate; a digital image capture means arranged to view the backlit image; a display screen for viewing by an operator; and an entry means for the operator to selectively make entries into the inventory and/or register system; computer storage and processing means having: a first memory store containing data representing a reference image of the viewplate; a second memory store containing a product database of data sets representing image descriptors characterising features of size and/or shape for all of the variety of known products; and digitised image processing and analysing means for processing and analysing a digitised image captured by the digital image capture means, including: means for using the data in the first memory store to subtract the representation of the reference image; means for generating at least one image descriptor characterising features of size and/or shape of the item to be identified; and means for comparing the at least one image descriptor with the stored data in the second memory store to identify stored data sets with the highest correlation and to produce a recognition result; and means for communicating the recognition result to the display screen.

In a preferred form, the entry means is provided by a touchscreen. In an alternative form, the entry means may be a barcode scanner.

The apparatus may include a plurality of identification stations, the computer storage and processing means having a first memory store associated with each identification station and a central second memory store for communication with each of the plurality of identification stations.

The identification station may include a weighscale associated with the viewplate, the product database including data characterising the weight of all of the variety of known products, and the comparison means including means to compare weight data from an item with stored weight data in the product database.

The invention also provides a checkout station in a retail goods outlet incorporating the above-defined item recognition apparatus.

Also within the contemplation of the invention is a system including the abovedefined item recognition apparatus in combination with an inventory and/or register system.

To more clearly illustrate the invention, an exemplification will now be described with reference to the accompanying drawings, in which: Figure 1 shows a schematic representation of an item recognition viewplate at a POS checkout station, on which a typical item is placed; Figure 2 is a block diagram illustrating an overview of the software system performing the recognition function; Figure 3 is a block diagram illustrating the image processing stage of the system; Figure 4 is a block diagram illustrating the analysis stage of the system; Figure 5 is a block diagram illustrating the recognition stage of the system; Figure 6 is an overall view of the POS checkout station as seen by a checkout operator; Figure 7 shows a state transition diagram for an operator interface; and Figure 8 shows an example view of the operator interface.

At the POS checkout station a planar viewplate 10 is horizontally arranged, upon which viewplate a checkout operator (or customer) places an item to be recognised 11, such as a bolt. Viewplate 10 is moulded with a shallow raised edge to stop items from rolling off and is formed from white acrylic sheet or some other suitable light-diffusing material. Viewplate 10 is illuminated from below by one or more fluorescent tubes 12 (although any other suitable light source may be used), to provide backlit illumination rendering a shadow or silhouette image to a viewer above viewplate 10. A black-and-white digital imaging camera 13 with a resolution of 800 x 600 pixels is mounted approximately 60cm above viewplate and focussed thereon, to capture a 2-D image when item 11 is placed on the viewplate, and thus within the focal plane of camera 13. An IR filter together with a UV filter is applied over the lens of camera 11 to reduce the effect of external lighting on the system. It should be noted that viewplate 10 may be incorporated in a wide variety of checkouts or other locations in retail outlets, or at different points in a warehouse or distribution station.

Camera 13 is connected to a software system 20 performing the item recognition function, which comprises three main stages; image processing 30; image analysis 40; and recognition 50. The other components illustrated in Figure 2 are a background image store 21 and a parts database 22, the purpose and operation of both components being explained in greater detail below. The output of the recognition system is passed to a checkout display 23. Additionally a signal from weight data means 24, such as a scale associated with viewplate may be provided to the recognition system for additional discrimination information.

Image Processing Stage 30 (Figure 3) At image capture step 31 the 2D shadow image captured as a digital representation is first converted (if necessary) to a form suitable for processing as a grey-scale image. The next step is background removal 32, in which the system compensates for image artefact or noise due to scratches or other marks present on viewplate 10. For this calibration, black-and-white camera 13 regularly captures a reference image when no items are present for calibration, and 6 thereby continually updates a background image store 21. Background removal step 32 subtracts the reference image from the grey-scale image to remove the background artefact from the image. The resultant data then passes through a binary threshold step 33, which operates to convert the grey-scale image to a binary image wherein each pixel is associated with a or a depending on whether the item was or was not present in the object area corresponding to that pixel. The final step in image processing stage 30 is image filtering 34 in order to remove noise and errors associated with the background removal and binary threshold operations. This image filtering step involves a standard smoothing operation "opening" or "closing" image pixels according to an algorithm which takes into account the status of neighbouring pixels. Such image processing methods are well known to the skilled reader and will not be further described here. The output from this step 34 is a filtered binary image Analysis Stage 40 (Figure 4) The aim of this stage is to determine characteristic feature information from the binary image 35 outputted from image processing stage 30. The first step here is object segmentation 41, enabling the system to handle more than a single item at once. This involves isolating the image associated with each item 11 from the rest of the image using a so-called 'region-growing' algorithm, and labelling that segmented image. Object segmentation of image data is well known to the skilled reader and will not be further described here. The next step is object selection 42, in which the system makes a selection from the segmented images of the most likely one required for recognition based on size, shape and position criteria. The selected object is then analysed at object analysis step 43 and a large number of topological and structural features are generated, these features becoming the basis of a descriptive feature vector 44 that is used to uniquely identify each product.

The features generated include, but are not limited to, those appearing in Table 1.

Image Feature Comment Width Length perpendicular to width Area a summation of the number of pixels Occupation Ratio within a rectangular bounding box Circularity Perimeter a summation of the edge pixels of the image Eccentricity Number of "Holes" a standard washer, say, would have one "hole" Size of "Holes" Boundary description features based on encoding vectors generated around the edge of the image Radial diameters signature Minimum Radial diameters the radial diameters at regular angular spacings Maximum Radial diameters all meeting at the centre of gravity of the image Mean of Radial diameters Standard deviation of Radial diameters Radial radii signature Minimum Radial radii similar to the radial diameter features, but Maximum Radial radii producing distinction for non-symmetrical items Mean of Radial radii Standard deviation of Radial radii First Hu Moment Second Hu Moment Third Hu Moment Standard area moments Fourth Hu Moment Fifth Hu Moment Sixth Hu Moment Seventh Hu Moment Table 1 Descriptors Feature vector 44 represents a weighted combination of these descriptors, the weighting being selected according to the particular application. It is to be noted that all these features are orientation-neutral, and it is therefore not necessary for the image processing to attempt to rotate the image before analysis step 43.

Item Recognition Stage 50 (Figure 8 The next stage utilises a combination of artificial intelligence and machine learning in a recognition step 51, in order to determine the most likely product matches based on descriptive feature vector 44, by correlation with the product data stored on parts database 22. The recognition step involves a pattern recognition technique, and the preferred technique is the K-Nearest Neighbour algorithm, since it is relatively fast to train. The K-Nearest Neighbour algorithm per se is well known to the skilled reader and will not be further described here.

Other approaches which may be more appropriate in particular applications are statistical techniques (Euclidean and other distance measures), combinations of Neural network and Fuzzy classification techniques, and search tree pruning techniques. These approaches are generally known in the field of pattern recognition, and the actual technique selected is likely to depend on the relative speeds of training and running the algorithms, as well as on the performance of those algorithms. If frequent updates to the product data are needed, then a technique which is fast to train is likely to be more appropriate than one that may be fast to run but comparatively slow to train.

The product data on parts database 22 is encoded under equivalent conditions to those prevailing at the POS. The recognition step results in a list of candidates, and a results sorting step 52 then generates a product candidate list, which may be, say, a top-5 or top-15 list. Finally these candidates are passed to a display system 23 for appropriate display of a visual representation of the products in the candidate list to a checkout operator, the visual representation being a sample image of each product, a description and an associated barcode from parts database 12.

An additional parameter to enhance product discrimination and/or to speed up the recognition process is the item weight value, which data may be captured by means of weight data means 24 such as a scale associated with viewplate The weight of an item is a physical attribute that can vary widely from product to product and therefore provide a very valuable recognition parameter, and minimise the error rate, in situations where size and shape may vary between some different items to a minimal extent. The weight data is passed to 9 recognition stage 50 and used as part of the correlation data against weight data stored in product database 22 in generating the product candidate list. Clearly other physical parameters may additionally or alternatively be used in the appropriate circumstances to enhance the discrimination process, such as additional visual data (eg laser scanning or additional camera views from different angles), magnetic properties, acoustic characteristics, spectral fingerprint, etc.

If the system fails to match the item on the viewplate with any part from the product database (either because the part is not in the database or due to problems arising from placement, lighting or calibration), the operator can be given the option of cancelling the entire operation and reverting to a simple manual or on-screen product catalogue.

The item recognition system is embodied in a software programme on a PC or other local computer system, which may be linked to a centralised parts database 22, as part of a network of stations. Alternatively parts database 22 may be local to the checkout station and updated regularly by network download or by means of a CDROM.

In use (see Figure the item 11 to be identified is placed on viewplate 10, which automatically triggers the image capture, processing, analysis and recognition steps as well as, if appropriate, a weight-value capture step. The resulting candidate list generated is then displayed on display 23. The customer or checkout operator then visually verifies the correct match presented from those displayed, and may have the operation of scrolling through pages of further candidates if the correct result is not found on the first page. The operator then uses a conventional hand-held checkout scanner to scan the associated barcode appearing on the display, or it may be preferred to circumvent the use of the barcode scanner by providing, say, a cursor- or touchscreen-operated selection system to allow the operator to enter the selected product directly on the display screen. The system may alternatively automatically send an identification to the computerised inventory/register system (such as the UPC number of a product) when the correlation between an item and a particular product profile stored on parts database 22 exceeds a certain "definite match" threshold. However it is thought that, since the checkout operator will be using a barcode scanner to enter other, larger items, for which the present invention might not be appropriate, then the use of screen barcodes will be more convenient and avoid undesirable interruption in the entry of a basket of diverse items. Furthermore there may be items of indeterminate topography without barcodes (such as cut lengths of rope in a hardware store) which will need individual attention and selection from an onscreen product catalogue by the checkout operator.

It has been found to be preferable in many situations to use an infra-red light source 12 (such as an array of IR-LEDs), detectable by camera 13, in order to minimise the effect of extraneous light sources on the image capture step. This is particularly the case when the objects to be recognised may be metallic, and therefore difficult to illuminate without introducing unwanted reflection.

In addition to the type of light source 12 employed, the light intensity emitted by the source has to be optimised. The selected source intensity is a factor of the viewplate properties and dimensions, the form of illumination used, the distance between the light source and viewplate, the camera lens filter used, as well as the sensitivity of the camera and image capture equipment. Ideally a small aperture is used for camera 13 in order to reduce the sensitivity to the effect of external light sources, and also to increase the depth of field, and therefore a higher intensity of illumination is generally preferred.

In a system tested (see below), an optimal distance between an IR-LED board and the viewplate, required for a clear and uniform background image, was found to be 8 cm.

In a form of the invention tested by the inventors, a touch-enabled colour 800 x 600 LCD panel (touchscreen) is used as display 23, for both viewing item 11, for displaying the resulting product candidate list, and for operator entry of system commands and product selection. The interface transition diagram of Figure 7 diagrammatically illustrates the use of this panel, whilst the sample panel display 11 shown in Figure 8 shows a page of a resulting candidate list as well as the available operator commands.

A single item recognition apparatus is arranged to support two neighbouring cash registers, one on either side of the apparatus, and an operator can decide to which cash register a selected product identification (eg. a machine barcode) is sent by making a selection either on the right hand side or the left hand side of the display screen (see below).

To begin use of the apparatus on a new item, the operator first clears viewplate and touches screen 23. This switches display 23 to provide a live video feed from camera 13. The operator places item 11 to be identified on viewplate ensuring the part is stationary and the image appears completely within the boundaries of the screen display. The operator then has the choice of selecting a MENU option by touching a part of the touchscreen (in which case a main menu is displayed allowing the operator to choose further options or to shut down the system), or to touch any other part of the image screen to begin the item recognition process.

After a short delay, the touchscreen display 23 then displays a new screen (Figure 8) including the product candidate list and selectable screen buttons. The candidates 60 are displayed as colour high-quality images together with a product description for each image, in descending order of likelihood in accordance with the result of applying the computer pattern recognition algorithm. If the correct result is not found on the first page, the operator may press button 64 to scroll forward through further pages, the page number being displayed in a corner of the screen 63. On the final page a CATALOGUE button will be available, enabling the operator to switch to a catalogue display if the correct product does not appear amongst candidates 60. If the system fails to match item 11 for any reason, the operator can press CANCEL button 64 to cancel the operation and try again, or alternatively switch to the CATALOGUE option.

12 When the operator successfully locates the correct product from candidates the desired selection is made by pressing on the appropriate button 61 or 62, depending on whether the selection is made for an entry in the right hand register or the left hand cash register. Once the selection is made, item 11 is then removed from viewplate 10 and touchscreen 23 is touched once more to return to the live video feed signal display.

It is to be noted that the system may be set up to recognise multiple items placed on viewplate 10, by applying the analysis and recognition steps to each of a number of segmented images identified from a single captured image.

It is to be understood that various modifications, alterations and/or additions may be made to the embodiments specifically described and illustrated herein without departing from the spirit and scope of the invention.

Claims (3)

1. Item recognition apparatus for recognising an individual item from a variety of known products for purposes of entry into an inventory and/or register system, the apparatus including: an identification station having: a viewplate with backside illumination to provide a backlit image viewable from the frontside of the viewplate; a digital image capture means arranged to view the backlit image; a display screen for viewing by an operator; and an entry means for the operator to selectively make entries into the inventory and/or register system; computer storage and processing means having: a first memory store containing data representing a reference image of the viewplate; a second memory store containing a product database of data sets representing image descriptors characterising features of size and/or shape for all of the variety of known products; and digitised image processing and analysing means for processing and analysing a digitised image captured by the digital image capture means, including: means for using the data in the first memory store to subtract the representation of the reference image; means for generating at least one image descriptor characterising features of size and/or shape of the item to be identified; and means for comparing the at least one image descriptor with the stored data in the second memory store to identify stored data sets with the highest correlation and to produce a recognition result; and means for communicating the recognition result to the display screen. 14

2. Apparatus according to claim 1, wherein the identification station includes a weighscale associated with the viewplate, the product database including data characterising the weight of all of the variety of known products, and the comparison means including means to compare weight data from an item with stored weight data in the product database.

3. Apparatus according to claim 1 or claim 2 wherein the entry means includes a touch screen. Dated: 23 May 2001 Phillips Ormonde Fitzpatrick Patent Attorneys for: AJAX COOKE PTY LTD