Method and apparatus for decoding two-dimensional symbols in the spatial domain

Abstract: A method and system for decoding a two-dimensional optical symbol in the spatial domain. The system determines the orientation of an imaged symbol in the spatial domain by using a template to derive a correlation coefficient between radially adjacent signals at a given angle of the template and comparing the correlation coefficients as the template is rotated with respect to the image. The angle of the highest correlation coefficient corresponds to the orientation of a set of symbol axes which include contiguous elements whose centers are collinear. The system then locates each individual information-encoded element of the symbol by using segments of the symbol defined by the axes, first adjusting for each segment the axis position and element size for subsequent determination of the location of all elements for decoding. The system is disclosed as applied to a symbol constructed as a matrix of hexagonal information-encoding elements arranged in a square, with, at its center, an acquisition target consisting of six concentric rings of alternating light and dark reflectivity. Also disclosed is a method for locating an acquisition target consisting of a plurality of concentric rings of alternating levels of reflectivity (bulls-eye pattern) in two-dimensional symbologies. Such targets may be found even if they vary in size and tilt angle with respect to an imaging camera. Symmetry characteristics of the targets are used to locate the targets independent of the threshold level selected for determining transitions between rings of different reflectivity.

Description: TECHNICAL FIELD The present invention relates to optically encoded symbologies, and more particularly relates to decoding a two-dimensional optical symbol in spatial domain. BACKGROUND OF RELATED ART Optical symbology refers to an information encoded symbol that is only readable by an optical sensing mechanism through a decoding process. For years, the bar code has been promoted as a machine-readable symbology. A bar code is a one-dimensional symbol that contains a unique serial number encoded in black and white bars. Bar codes are widely used as indices to associate physical objects with larger databases containing more detailed information. One example of a system for processing an image including a bar code is shown in EP-A-0 449 634, which discloses determining the fine orientation of a bar code by dividing the image area into vertical cells 52, some of which may be superimposed over a portion of a bar code label. For each scan line within the cell, a signal corresponding to the gray level of the pixels along that scan line in the cell is obtained (Fig. 8c), as well as the offset between the signals generated by different scan lines (Fig. 8d). A cross-correlation function (Fig. 9) of the first scan line of the cell with each of the successive scan lines is used to determine the orientation of the bars of the bar code relative to the scan lines. As the demand for information-based technologies grows, there is considerable interest in eliminating the associated database and storing more data information in the bar code itself. With current technology, encoding additional information makes a one-dimensional bar code unrealistically long. A number of two-dimensional symbologies, either in the form of a stacked bar code or a matrix type, have been introduced to accommodate this need. A two-dimensional symbol is composed of a two-dimensional array of elements which comprise information cells. The size of the array, or the size of a symbol is then determined by the encoding procedures and the information to be encoded. The cells in the symbol are generally polygonal in shape and appear to be as either white or black. Thus, a symbol is comprised based on the geometric shape of the cells and the structures. All elements, either white or black, in a symbol are equal-sized and equal-shaped. The primary objective of using a two-dimensional symbology is to eliminate the associated database as used in bar code applications. Thus, the symbol becomes a portable information carrier that is database independent. The feature of being an information carrier is particularly important in transportation and distribution industries in which letters, packages, containers, and other related items that are shipped or transported are generally identified by the origin, flight number, destination, name, price, part number, and other information. The information is encoded into a label which is read along the distribution route. Other applications for these labels include automatic routing and sorting of mail, parcels, and baggage. One of the two-dimensional symbologies, publicly known as MaxiCode symbology, is disclosed in EP-A-0 573 129 and in U. S. Patents No. 4,874,936 and 4,896,029 to Chandler and et al. The disclosed symbol is an example in which the cells are hexagonal in shape and approximately 30 mils in diameter. To decode such a symbol, one has to determine the reflectance intensity on each element. To correctly determine the gray scale intensity of each hexagonal element, first the position and orientation of the symbol must be found, and the location of the center of each element identified. EP-A-0 573 129 also discloses an acquisition target formed of a plurality of concentric rings of contrasting reflectivities, which will yield a periodic video signal when scanned linearly. A method of finding the acquisition target uses analog filter means, the signal being compared directly with a predetermined frequency, allowing matching of the frequencies. Despite this method, there has further been a need in the art for a system of reduced computational complexity for locating two-dimensional acquisition targets, which at the same time is more efficient and more reliable. There also has been a need for such a system that can efficiently locate an acquisition target regardless of its size. SUMMARY OF THE INVENTION The present invention has been made in consideration of the above described problems. According to an aspect of the invention, there is provided a method of locating an optically readable, two-dimensional acquisition target in a stored image as defined by claim 1. The invention also relates to a system as defined by claim 9. Generally described, the system and method locate a key element by scanning the stored image along a first scanning direction, detecting a candidate bounded element and associated border area demonstrating symmetry along the first scanning direction, and determining whether the candidate bounded element and associated border area are symmetric along a second direction different from the first direction. The advantage of using symmetry tests is that they are invariant to distortion of the image of the acquisition target due to a tilt angle between the target and the sensing mechanism since the symmetric characteristic of the acquisition target is preserved in the distorted image. Preferably, the system and method also include determining diameters of the candidate bounded element and its associated border area in each of the directions in which they exhibit symmetry; and determining whether the diameters are within a predetermined tolerance of each other. The step of detecting symmetry along the scanning direction preferably comprises run-length coding the rows of the image, and examining the runs for symmetry. Therefore the present invention seeks to provide an improved system for locating an acquisition target in a digital image, such as an image of a two-dimensional symbology. Other objects, features and advantages of the present invention will become apparent upon examining the following description of the preferred embodiment of the invention, when taken in conjunction with the accompanying drawings and the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a plan view of a two-dimensional symbol with indicated six axes. Figure 2 shows the system flowchart outlining the decoding of the symbol in spatial domain. Figure 3 is a system block diagram for imaging a two-dimensional acquisition target or symbol in which the processor is configured according to the present invention. Figure 4 shows the axes used to test the symmetric features of the acquisition target in the preferred embodiment of the invention. Figures 5A and 5B show a system flowchart outlining the steps of the technique of the present invention for locating an acquisition target. Figure 6 shows diagrammatically a fine-tuning procedure for determining the center of a key element. DETAILED DESCRIPTION OF THE INVENTION Referring now to the drawings, in which like numerals refer to like parts throughout the several views, Fig. 1 shows a prior art two-dimensional symbol 10, known as MaxiCode, which may be positioned on a label, package or the like. The MaxiCode symbology includes a matrix of hexagonal information encoding elements 12 arranged in a square, and, at its center, an acquisition target 100 consisting of six concentric rings of alternating light and dark reflectivity. Fig. 2 is a flow diagram showing the overall process of the present invention for obtaining an image of the symbol, detecting its bull's eye acquisition target, estimating the size of hexagons, determining threshold levels for distinguishing black hexagons from white hexagons, determining the orientation of symbol axes contained within the MaxiCode symbol, fine tuning values for axis direction and hexagon size, and locating the centers of individual hexagons for decoding of the information contained within the symbol. Reference will be made to this overall flow diagram as the subcomponents of the process are described in detail. Reader Hardware Referring to Fig. 3, the components of a sensing mechanism 20 are shown diagrammatically. The purpose of the sensing mechanism 20 is to acquire an image of indicia on a surface such as a label 14. This mechanism carries out steps 202, 204, and 206 as indicated in Fig. 2. The sensing mechanism 20 includes a CCD (charge-coupled device) video camera 30, such as a Pulnix 7-CN manufactured by Pulnix America, Inc. of Sunnyvale, California 94086. The target is illuminated by a light source, such as one or more LEDs 22, and the reflected light from the symbol 10 on the label 14 is focused by optics 24 onto the detector of the camera 30. Such a camera includes a matrix of pixels which form an image by providing an output signal related to the amount of light falling on the pixel. These output signals are conventionally read out to an analog-to-digital converter 32 to generate an array of image data which may, for example, range from 0 (black) to 255 (white), and to store such data in a computer memory 34 under control of a microprocessor 36. The process of obtaining such image data in computer memory is described in U.S. Patents No. 4,874,936 and 4,896,029. The present invention is preferably implemented in software in C language running on a personal computer such as Compaq Deskpro 50M with an image grab card therein. The image grab card provides an interface between the CCD camera and the computer. The image grab card used in the present invention is a PowerGrabber marketed by Dipix Technologies of Canada. The processor 36 in the computer may be programmed by a person of ordinary skill to carry out the following operations on the image data stored in the memory 34. It should also be understood that the functions of the invention could be embodied in hardwired logic circuits or in application specific integrated circuits (ASICs). To access any pixel in the computer memory 34, two parameters, x representing column and y representing row, that is, coordinates (x, y), are used as an address to a pixel. Therefore, the digitized video signal can be viewed as a two-dimensional image. The x axis as shown in Fig. 1 is parallel to the rows of pixels of the CCD detector of the camera, but the camera may capture the symbol 10 at any angle relative to the x axis. Also, the camera may be tilted relative to the plane of the symbol, causing some distortion of the image. Acquisition Target Detection Of the six rings comprising the MaxiCode acquisition target, there are three dark rings 101, 103 and 105 and three light rings 102, 104 and 106, as shown in Fig. 4. The inner light core 102 is considered one of the three light concentric rings. The inner core 102 provides a key element and the other rings provide border elements, all of which can be used to locate the target in a manner described below. In the stored multiple-row image of the symbol 10, data representing the light rings have high numerical values due to the high reflectivity of the light rings and those data representing the dark rings have low numerical values due to the low reflectivity of the dark rings. According to the present invention, the location of an acquisition target is not done by matching a synthetic template. Instead, the location process (represented in step 208 of Fig. 2) is carried out within the image data itself by using the symmetric property of the acquisition target. Fig. 4 shows the same acquisition target 100 with four axes separated by 45 degree angles. An axis 300 points to the East or in horizontal direction, an axis 302 points to the North or in vertical direction, and axes 304 and 306 point to the Northeast and Northwest, respectively. Axis 300 is the same as the x axis shown in Fig. 1, and is defined as being parallel to the rows of pixels. In the preferred embodiment shown, the acquisition target is symmetric about any one of these axes. In fact, the symmetry is not limited to these axes in the preferred acquisition target, whose concentric circular rings are omni-directionally symmetric. Of course, references to direction herein, such as "North", or "vertical", are used only to provide a relative frame of reference. Referring now to Figs. 5A and 5B, the process of step 208 of Fig. 2 for locating the center of the acquisition target is shown diagrammatically in more detail with reference to corresponding processing devices. Additionally, to provide information for possible estimation of image resolution, the diameter of the target is obtained. In block 254 a starting row number in the image is selected. Thus, a row of pixel values is being addressed. A threshold for distinguishing white pixels from black pixels is determined from the set of values for the current row. The threshold is used to find transitions between black and white areas of the stored image. There are many methods of determining the threshold known to those in the art, such as the histogram method described in U.S. Patent No. 4,874,936. Preferably, a middle value computed as 1/2(P max + P min ) is selected as the threshold, where P max and P min are the maximum and minimum pixel values in the current row. Alternately, a threshold may be calculated in the same way for the entire image, but providing a separate threshold for each row is preferred for the process of locating the center of the acquisition target. A different method for determining the threshold is disclosed below in connection with determining the orientation of the symbol. Based on the calculated threshold, the data can be run-length encoded. The following is an example: BBBBBBBWWWWWWWWBBBWWWWWBBBBBWWWWBBBBBWWWW Those pixel values under the threshold are labeled by letter B (i.e., Black) and those over the threshold are labeled by letter W (i.e., White). The corresponding run-length encoded sequence is given by: 78355454. The numerical value representing the number of contiguous occurrences of the same letter in the immediate group is called a "run". The total number of runs in the example is 8. Note that the coding is always started with the first black pixel in the scanning direction. Thus, if one indexes the coded sequence by 1, 2, 3, . . . , then the runs indexed by even numbers stand for white runs. To detect numerically the possibility that a row passing through the center of the acquisition target has currently been encoded, the number of runs is compared with the number of rings at block 255. Along the axis 300 of the target 100, which crosses the five bordering rings 101, 103, 104, 105, 106 twice and the center ring 102 once, there are at least 12 "W to B" or "B to W" transitions. Thus, for a row going through the center white ring 102 of the given target 100, the number of runs must be greater than 13. If the number of runs is less than a predefined number (preferably 13), there is no need to further test for symmetry of the target 100 along this row. In this case the program proceeds to block 259 and tests to determine whether the current row is the last row in the image. If so, a message is generated in block 261 that no target was found in the image. If not, the program returns to block 254 to examine the next row. It will be understood that the predefined minimum number of runs is dependent upon the number of concentric rings comprising the target. If the number of runs is greater than the predefined number, the program examines in turn each white run in the current row. Due to the pattern of this acquisition target, the first two white runs and the last two white runs cannot be the center white run. Therefore, we can limit the search to the white runs which are respectively offset to the first and last white runs of the coded sequence by a certain number, (e.g., 2). At block 256, the first white run is selected. At block 258, the run is tested to determine if it is already at the end of the current row. If so, the program proceeds to block 259 for the test described above. If not, at blocks 262 and 264 the program tests to determine if the white run represents the inner ring 102. For an image of fixed size, the size of the acquisition target can not be arbitrarily big. A certain maximum length (e.g. 6 pixels) can be imposed on the length of the inner white run in order to speed up the locating process. If the white run is longer than this tolerance, tested at block 262, then there is no need to continue testing this run for symmetry, and the program returns to block 256. If the test result shows the white run is within the tolerance, a further test at block 264 is carried out to compare the white run with the proceeding two white runs and the following two white runs. Note that if the three light rings by design have the same width, as is the case in the preferred target shown in Fig. 4, the width of their corresponding runs may differ from each other by one or at most two pixels as a result of the thresholding process or insufficient image resolution. There are different ways to compare the similarity between the current white run and the neighboring white runs. One similarity test in the preferred embodiment is to see if the center white run plus one pixel is at least as long as each of its four neighboring white runs. It should be clear that this test is not sensitive to any variation in the black/white pixel threshold described above (that is, the choice of the threshold is not critical). If the center white run plus one pixel is shorter than any one of its four neighboring white runs, then it can not be the representative of the inner ring 102, and the program returns to block 256. If the current run satisfies the test of block 264, at block 268 the following symmetry test is carried out, given an acquisition target 100 formed of 6 concentric rings originally printed with roughly identical width: (1) Let l be the length of the current white run. Let w 1 , w 2 , w 3 , w 4 be the two preceding and two following white runs, and let b 1 , b 2 , b 3 , b 4 be the two preceding and two following black runs, of the current row. Denote the average lengths of white runs and black runs respectively by w and b . Then , w = w 1 + w 2 + w 3 + w 4 4 ; and b = b 1 + b 2 + b 3 + b 4 4 . The symmetry test is satisfied if the two following conditions are met: | b - w | < 2 | w j - w | < 2 and | b j - b | < 2 for j = 1,2,3,4. where 2 represents the tolerance (2 pixels) allowed for variations in label printer toner and inaccurate image threshold level. Note that the outer dark ring 105 is not used in the test, because its width may sometimes be affected by noisy components (e.g. dark symbol components - hexagons in the case of MaxiCode symbology) adjacent to it. If the foregoing first symmetry test is failed, the program returns to block 256. After passing the symmetry test along axis 300, at block 272 a candidate center coordinate (x 1 , y 1 ) of the acquisition target is declared and the estimated diameter of the entire target along axis 300 (still excluding the outer dark ring 105) is estimated as: Based on the candidate center (x 1 , y 1 ), further symmetry tests are carried out at blocks 274 to 282 to verify that the coordinates (x 1 , y 1 ) are within the center of the acquisition target. A new direction for symmetry testing is selected at block 274. Starting from the pixel (x 1 , y 1 ), and using the same threshold as is used in the current horizontal direction, run-length coding both downwardly and upwardly along axis 304 is performed until a total of five "W to B" or "B to W" transitions in each direction are obtained. This results in four white runs and four black runs centered at the candidate center white run. The foregoing symmetry test is applied to these runs at block 276, and the diameter of the acquisition target along this axis is computed and saved at block 278. It will be understood by those skilled in the art that this diagonal diameter along axis 304, the number of pixels multiplied by a factor of √ 2 , should be closely related to the one previously computed along axis 300. The difference between these two diameters is directly proportional to the tilt angle which causes image distortion. A tolerance can be set according to the maximum tilt angle allowed in the imaging process. For example a tolerance of 4 pixels works for tilt angle up to 30 degrees. If the difference is bigger than the selected tolerance, tested at block 280, then the alleged center (x 1 , y 1 ) is discarded and the program returns to block 256 to address a new run, and eventually, if necessary, a new row of data for run-length encoding to repeat the above described tests. Otherwise, the program returns to block 274, and the symmetry test and diameter check will be continued along axes 306 and 302, respectively. Finally, if the vertically encoded data along axis 302, passing through the candidate center (x 1 , y 1 ), pass the symmetry test and the diameter check, the alleged center (x 1 , y 1 ) is confirmed at block 284. In the embodiment shown, any failure will return the program to block 256. However, to allow for distortion or for imperfections in the image of the acquisition target, one failure may be permitted before abandoning the candidate run. For example, failure along one of the diagonal axes may be tolerated if the other diagonal axis and vertical axis pass the symmetry and diameter tests. It will be understood by those skilled in the art that the candidate center (x 1 , y 1 ) must now lie in the inner light ring 102, yet it may not be in the true center of the acquisition target. A fine-tuning procedure at blocks 286 and 288 is executed based on the starting coordinates (x 1 , y 1 ). Figure 6 shows the tuning procedure diagrammatically. The coordinates (x 1 , y 1 ) are within the inner ring 102 and assumed to lie at pixel 402. Given y 1 , a tuning window is defined. The size of the window is defined by the size of the inner white ring 102. The lengths of at least 2 rows of data (the length of the white run) within the inner ring 102 just above and below y 1 are counted, such as row y 0 , y 2 and y 3 . Numerical values representing the corresponding lengths are then obtained, for example the length of row y 0 may be 5 pixels and the length of row y 2 may be 7 pixels. The new vertical coordinate is chosen in the longest row, that is, in the row measuring the greatest width of the inner ring 102. This establishes a new candidate center 404 (which in the example shown is at coordinates (x 1 , y 2 )). Then, the same type of measurement is again performed along the columns based on the coordinates (x 1 , y 2 ), to find the longest column within the inner ring 102 (which turns out to be x 2 in the example shown in Fig. 6). The intersection of the longest row and the longest column, at coordinates (x 2 , y 2 ) are the final or true center coordinates of the acquisition target. When there are two or more column or row length values that happen to be the same, at block 288 of Fig. 5 the pixel intensity values for each of the equal rows or columns are accumulated. The final center coordinate in such a case is selected to be in the row or column having the largest summed intensity value. There is a fixed size relationship between the overall MaxiCode symbology, the rings of the acquisition target, and the individual hexagons. Therefore, it will be understood that upon determining an estimate of the size of the acquisition target as described above, the average size of the individual hexagons in the image can be calculated. This may be expressed in terms of the average hexagon diameter, which is the distance between opposed flat sides of a hexagon. In the MaxiCode symbology, in which the hexagons are packed together, the hexagon diameter is equal to the distance between the centers of adjacent hexagons. Thus, references to hexagon size may be taken to refer also to hexagon spacing. This would not necessarily be true for all two-dimensional symbologies. The present invention has been described in relation to particular embodiments which are intended in all respects to be illustrative rather than restrictive. Alternative embodiments will become apparent to those skilled in the art to which the present invention pertains without departing from the scope of the present invention which is defined by the appended claims rather than the foregoing description.

Claims: A method of locating an optically readable, two-dimensional acquisition target (100) in a stored image, said target being of the type having a key element (102), said key element being a geometrically symmetric, bounded element of a first reflectivity surrounded by a geometrically symmetric border area (101) of a second reflectivity, comprising the steps of: scanning (254-264) said stored image along a first scanning direction and detecting (268) a candidate bounded element and associated border area demonstrating symmetry along said first scanning direction; characterised by determining (274,276) whether said candidate bounded element (102) and associated border area (101) are symmetric along a second direction different from the first direction. The method of Claim 1, further comprising the steps of: determining (272,278) diameters of said candidate bounded element and said associated border area in said first scanning direction and said second direction; and determining (280) whether said diameters are within a predetermined tolerance of each other. The method of Claim 1, wherein said step of detecting a candidate bounded element and associated border area demonstrating symmetry along said first scanning direction comprises run-length coding (254) the rows of said image, and examining said runs for symmetry. The method of Claim 1, wherein said key border area comprises a plurality of symmetric rings (101,103-106) of alternating first and second reflectivity surrounding said key element; further comprising the steps of: finding (272) a center point of said candidate bounded element; determining (268,276) whether said candidate bounded element and associated border area are symmetric along at least one of a pair of diagonal directions passing through said center point; determining whether said candidate bounded element and associated border area are symmetric along a vertical direction normal to said first scanning direction; determining (278) diameters of said candidate bounded element and border area in each of said directions in which they exhibit symmetry; and determining (280) whether said diameters are within a predetermined tolerance of each other. The method of Claim 4, further comprising the steps of averaging said diameters and determining the size of said target (100) based on said average diameter. The method of Claim 4, wherein said steps of determining whether said candidate bounded element and said associated border area are symmetric comprise the step of determining whether the width of said bounded element (102) is at least as long as the widths of each of said border area rings (101,103-106) of alternating reflectivity on both sides of said bounded element. The method of Claim 6, wherein said steps of determining whether said candidate bounded element and said associated border area are symmetric further comprise the steps of: determining whether the absolute value of the difference between a first average width of the rings of the first reflectivity (101,103,105) on both sides of said bounded element and a second average width of the rings of the second reflectivity (104,106) on both sides of said bounded element is less than a preset tolerance; determining whether the difference between the first average width of the rings of the first reflectivity on both sides of said bounded element and the individual components of said first average is less than said preset tolerance; and determining whether the difference between the second average width of the rings of the second reflectivity on both sides of said bounded element and the individual components of said second average is less than said preset tolerance. The method of Claim 7, further comprising the steps of finding a pair of longest orthogonal dimensions (x i ,y i )within said bounded element and assigning a center point of said bounded element at the intersection of said longest orthogonal dimensions. A system for locating an optically readable, two-dimensional acquisition target in a stored image, said target (100) being of the type having a key element (102), said key element being a geometrically symmetric, bounded element of a first reflectivity surrounded by a geometrically symmetric key border area of a second reflectivity, said system including a light source (22); a detector (30) comprising an array of pixels positioned to receive light from said light source reflected by said target; a read-out circuit (32) connected to obtain data signals from said pixels and to form in a memory device (34) an image including said target; and a processor (36) configured to locate said key element by: scanning (254-264) said stored image along a first scanning direction to detect (268) a candidate bounded element and associated border area demonstrating symmetry along said first scanning direction; characterised by determining (274,276) whether said candidate bounded element and associated border area are symmetric along a second direction different from said first direction. The system of Claim 9, wherein said key border area comprises a plurality of symmetric rings (101,103-106) of alternating first and second reflectivity surrounding said key element; and wherein said processor is further configured to: find (272) a center point of said candidate bounded element; determine (268,276) whether said candidate bounded element and associated border area are symmetric along at least one of a pair of diagonal directions passing through said center point; determine whether said candidate bounded element and associated border area are symmetric along a vertical direction normal to said first scanning direction; determine (272,278) diameters of said candidate bounded element and border area in each of said directions in which they exhibit symmetry; and determine (280) whether said diameters are within a predetermined tolerance of each other. The system of Claim 10, wherein said processor is further configured to average said diameters and determine the size of said target (100) based on said average diameter. The system of Claim 11, wherein said processor (36) is further configured to determine whether the width of said bounded element (102) is at least as long as the widths of each of said border area rings (101,103-106) of alternating reflectivity on both sides of said bounded element. The system of Claim 12, wherein said processor (36) is further configured to: determine whether the absolute value of the difference between a first average width of the rings of the first reflectivity (101,103,105) on both sides of said bounded element and a second average width of the rings of the second reflectivity (104,106) on both sides of said bounded element is less than a preset tolerance; determine whether the difference between the first average width of the rings of the first reflectivity on both sides of said bounded element and the individual components of said first average is less than said preset tolerance; and determine whether the difference between the second average width of the rings of the second reflectivity on both sides of said bounded element and the individual components of said second average is less than said preset tolerance. The system of Claim 13, wherein said processor is further configured to find a pair of longest orthogonal dimensions (x i ,y i )within said bounded element and assign a center point of said bounded element at the intersection of said longest orthogonal dimensions. The system of Claim 9 wherein said processor is configured to scan said stored image along a scanning direction for non-character information, and to detect a candidate bounded element demonstrating symmetry along said scanning direction using said non-character information. The system of Claim 9, wherein said processor is further configured to: determine diameters of said candidate bounded element and said associated border area in each of said directions in which they exhibit symmetry; and determine whether said diameters are within a predetermined tolerance of each other. The system of Claim 9, wherein said processor (36) is further configured to run-length encode the rows of said image, and examine said runs for symmetry.

Inventors: LEI MING, ZHENG JOE, LEI, MING, ZHENG, JOE
Applicants: UNITED PARCEL SERVICE INC, UNITED PARCEL SERVICE OF AMERICA, INC.
Application year: 1995
Main classification: G06K7
IPC codes: G06K7