Patent Abstract:
An artificial vision inspection method and system which takes photographs, on either sides, of poultry or other meat at various stages of processing as they pass by on hanging racks. The method and system then sorts the meat according to quality parameters selected by the user, from presence or absence of parts to size to coloration.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATION 
       [0001]    The present application claims the benefits of U.S. provisional patent application No. 60/700,020 filed Jul. 18, 2005, which is hereby incorporated by reference. 
     
    
     TECHNICAL FIELD 
       [0002]    The present invention relates to an artificial vision inspection method and system. More specifically, the present invention relates to an artificial vision method and system for the inspection of carcasses, for example slaughtered poultry or other meat. The present invention further relates to a method and system for classifying the slaughtered poultry or other meat into predefined categories according to predefined inspection parameters. 
       BACKGROUND 
       [0003]    The processing of poultry or other meat in a slaughterhouse takes place using different machines, each of which carries out a specific operation on a bird or part of a bird. These machines, which, for example, cut off heads, cut off necks, eviscerate the birds and joint the carcass, are arranged in a logical sequence along conveyor lines, and thus form production lines along which the birds are conveyed, hanging by the two legs from a hook, in order to undergo the successive processing operations. 
         [0004]    The poultry supplied to the slaughterhouse is not uniform in body build, weight and/or condition, even if it comes from the same flock (a collection of birds raised together), which means, for example, that variations of up to 20% in the size of body parts may occur between individual birds coming from the same flock or reared under comparable conditions. 
         [0005]    On the other hand, a great variety of products is desired by the customers of the slaughterhouse. 
         [0006]    In order to make it possible to meet current customer demands in the optimum manner, bifurcations are fitted at certain points on the conveyor lines, which bifurcations are in general formed by automatic overhang machines which are known per se, and where according to the state of the art it is decided on the basis of the weight of each bird and/or on the basis of a visual inspection which conveyance route must be followed from the bifurcation. 
         [0007]    It is important here that the most suitable processing should be carried out on the birds on the machine most suitable for that purpose, resulting in the maximum production output. By the known method it is only possible to a very limited extent to guide each bird or part of a bird to the most suitable processing machine, i.e. at a bifurcation in a conveyor line to determine the most suitable path to control an automatic overhang machine, because the means for determining the characteristics of the birds (shape, size of the breast and/or the legs, injuries, condition, etc.) on the basis of which a decision has to be made are non-existent or, in the case of a visual inspection by quality control inspectors, are inadequate having on average a 30 percent margin of error due in particular to the high speeds at which the birds are conveyed along the conveyor line. 
       SUMMARY 
       [0008]    The present invention relates to a system and method for classifying a meat carcass, implementing the steps of:
       a. acquiring at least one digital image of the carcass;   b. processing the digital image;   c. verifying the processed digital image in order to detect the presence of at least one defect;   d. classifying the carcass in response to the presence or not of the at least one defect.       
 
         [0013]    The present invention further relates to a system and method for classifying a meat carcass as described above which activates an output associated with the classification of the carcass effected in step d. 
     
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         [0014]    Embodiments of the invention will be described by way of example only with reference to the accompanying drawings, in which: 
           [0015]      FIG. 1  is schematic view of an embodiment of the artificial vision inspection system; 
           [0016]      FIG. 2  is a flow diagram of a general algorithm for the inspection of slaughtered poultry or other meat; 
           [0017]      FIG. 3  is a digital image of a slaughtered poultry; 
           [0018]      FIG. 4  is a flow diagram of a general algorithm for the processing of the digital image of a slaughtered poultry; 
           [0019]      FIG. 5  is the digital of  FIG. 3  after being processed by the algorithm of  FIG. 4 ; 
           [0020]      FIG. 6  is a contour view of  FIG. 5  to which is applied a leg detection tool; 
           [0021]      FIG. 7A and 7B  is a flow diagram of an algorithm for detection of a missing wing or a wing drum in a slaughtered poultry; 
           [0022]      FIG. 8  is a flow diagram of a first embodiment of an algorithm for the determination of a reference point on the side of a slaughtered poultry; 
           [0023]      FIG. 9  is a flow diagram of a second embodiment of an algorithm for the determination of a reference point on the side of a slaughtered poultry; 
           [0024]      FIG. 10  is a contour view of  FIG. 5  to which is applied a wing detection tool; 
           [0025]      FIG. 11  is a contour view of  FIG. 5  to which is applied a wing detection tool; 
           [0026]      FIG. 12  is a flow diagram of an algorithm for the detection of holes in legs of slaughtered poultry; 
           [0027]      FIG. 13  is a contour view of  FIG. 5  to which is applied a hole detection tool; 
           [0028]      FIG. 14A and 14B  is a flow diagram of an algorithm for the detection of a skin condition in slaughtered poultry; and 
           [0029]      FIG. 15  is a contour view of  FIG. 5  to which is applied a skin condition detection tool. 
       
    
    
     DETAILED DESCRIPTION 
       [0030]    A non-restrictive illustrative embodiment of the present invention is concerned with artificial vision method and system for the inspection of slaughtered poultry or other meat and for classifying the slaughtered poultry or other meat into predefined categories according to predefined inspection parameters. 
         [0031]    Referring to  FIG. 1  of the appended drawings, the artificial vision inspection system  100  includes a conveyor  101  on which, for example, carcasses  102 ,  103 ,  104 ,  105 ,  106 ,  107 ,  108  are conveyed through a front and back inspection stations  109   a  and  109   b , respectively. Each carcass presents for inspection a front portion to the front inspection station  109   a  and a back portion to the back inspection station  109   b . The front inspection station  109   a  is concerned with the inspection of the front portions of the carcasses  102 ,  103 ,  104 ,  105 ,  106 ,  107 ,  108 , for example the front portions  103   a  and  107   a  of carcasses  103  and  107 , respectively, and the back inspection station  109   b  with the inspection of the back portion of the carcasses  102 .  103 ,  104 ,  105 ,  106 ,  107 ,  108 , for example  103   b  and  107   b . For the sake of clarity only the front inspection station  109   a  will be further described but it is to be understood that the back inspection station  109   b  includes components similar to the components of the front inspection station  109   a . The components of the back inspection station  109   b  are identified by similar numerals as those for the front inspection station  109   a  but ending with a “b” instead of an “a”. 
         [0032]    The front inspection station  109   a  includes an enclosure  110   a , which may be made of, for example, stainless steel, having a non-reflective backdrop  111   a , a breakout board  112   a  and a sensor  116   a  connected to the breakout board  112   a  using connector  117   a . Within the enclosure  110   a  are positioned a digital camera  114   a  and diffuse light sources  118   a , which are connected to the breakout board  112   a  using connector  115   a  and  119   a , respectively. The purpose of the enclosure  110   a  is to block ambient light so that the carcasses  102 ,  103 ,  104 ,  105 ,  106 ,  107 ,  108  may be uniformly illuminated by the diffuse light sources  118   a . As for the backdrop  111   a , its purpose is to provide a uniform and contrasting background on which digital images of the carcasses  102 ,  103 ,  104 ,  105 ,  106 ,  107 ,  108  may be taken by the digital camera  114   a.    
         [0033]    The breakout boards  112   a  and  112   b  of the front and back Inspection stations  109   a  and  109   b , respectively, are both connected to a main board  120  through connectors  122   a  and  122   b , respectively. In turn, the main board  120  is connected to a sorting station (not shown), through connector  124 . The sorting station directs, upon receiving a signal from the main board  120 , which path each of the carcass is to take for further processing. Optionally, the main board  120  may be connected to a PC or monitoring station  126 , through connector  128 . Furthermore, the digital cameras  114   a ,  114   b  may also optionally be connected directly to the main board  102  (connector not shown) in order to provide digital images that may be stored for surveillance, further processing, inventory, statistical analysis, etc. 
         [0034]    In the illustrative embodiment the breakout board  112   a  may be a CON-IBOB from DVT, the digital camera  114   a , a 552CW from DVT, the sensor  116   a  an E37-T86 photoelectric sensor from Omron and the diffuse light sources  118   a  IDWA-D strobe fights from DVT. 
         [0035]    It is to be understood that the various connectors  115   a ,  117   a ,  119   a ,  122   a ,  124  and  128  may be any suitable wired or wireless communication technologies. 
         [0036]    In operation, whenever a carcass, for example carcass  103 , enters the front inspection stations  109   a  along the conveyor  101  and passes in front of the sensor  116   a , the sensor  116   a  sends a signal to the breakout board  112   a , which synchronizes the activation of the digital camera  114   a  and the light sources  118   a  in order to acquire a digital image of the front portion  103   a  of the carcass  103 . The camera  114   a  includes a processor with an inspection algorithm, which will be detailed further below, that analyses the image of the front portion  103   a  of the carcass  103  and provides classification data to the breakout board  112   a , which in turn provides the signal to the main board  120 . Similarly, when the carcass, for example carcass  107 , enters the back inspection station  109   b  the same chain of events takes place for the back portion  107   b  of the carcass  107  and the information is provided to the main board  120 . The main board  120  receives classification data from both the front  109   a  and the back  109   b  inspection stations, and using that data activates proper bifurcation devices in order to route each of the carcasses  102 ,  103 ,  104 ,  105 ,  106 ,  107 ,  108  on the path corresponding to its classification. 
         [0037]    A possible inspection algorithm that may be executed by the processor of the digital camera  114   a  is depicted by the flow diagram shown in  FIG. 2 . The steps of the algorithm are indicated by blocks  202  to  216 . The algorithm may be in the form of a script using, for example, tools found in the Intellect software from DVT. Thus any reference to “tools” refers to tools present in the Intellect software. It is to be understood that other software with tools having similar functions may be used. Furthermore, although the following description of the algorithm makes reference to the front portion of a carcass, it is to be understood that the algorithm applies to its back portion as well. 
         [0038]    At block  202  the algorithm starts by acquiring the digital image of the carcass, for example a 640 by 480 pixel image  200  of the front portion  103   a  of carcass  103 , such as shown in  FIG. 3 . The image  200  is acquired each time the breakout board  112   a , in synchronization with the sensor  1165 , activates the digital camera  114   a  and associated diffuse light sources  118   a . At block  204 , the digital image  200  acquired at block  202  is processed to enhance it and provide a high contrast image  201  of the front of the carcass  103   a , such as shown in  FIG. 5 , in order to facilitate the detection of various features. The processing of the acquired digital image  200  into the high contrast image  201  will be detailed further below. 
         [0039]    Then, at block  206 , the algorithm detects if a carcass is present or not as the sensor  116   a  may have been activated by the presence of the tip of a wing from a following carcass. If no carcass is detected, the algorithm registers the defect and goes to block  216  to activate the output. It a carcass is detected, for example carcass  103 , the algorithm proceeds to block  208 . 
         [0040]    At block  208 , the algorithm detects if the carcass  103  is missing a leg. If a missing leg is detected, the algorithm registers the defect and goes to block  216  to activate the output. If both legs are detected, the algorithm proceeds to block  210 . 
         [0041]    At block  210 , the algorithm detects if the carcass  103  is missing a wing or if a wing drum is present. If a missing wing or a wing drum is detected, the algorithm registers the defect and goes to block  216  to activate the output. If both wings are detected, the algorithm proceeds to block  212 . 
         [0042]    At block  212 , the algorithm detects the presence of a hole in one of the legs of the carcass  103 . If a hole is detected, the algorithm registers the defect and goes to block  216  to activate the output. If no holes are detected, the algorithm proceeds to block  214 . 
         [0043]    At block  214 , the algorithm detects the presence of one or more skin condition on the skin of the carcass  103 . If a skin condition is detected, the algorithm registers the defect and goes to block  216  to activate the output. If no skin condition is detected, the algorithm proceeds to block  216 . 
         [0044]    Finally, at black  216 , the algorithm classifies the front portion  103   a  of the carcass  103  as a pass or a fail depending on whether a defect has been detected or not. The digital camera  114   a  then forwards its classification data to the breakout board  112   a  which in turn communicates it to the main board  120 . The main board  120  having classification data for both the front and back portions of a given carcass may then activate proper bifurcation devices in order to route the carcass an the path corresponding to its classification. The algorithm then goes back to block  202  where a new digital image is acquired. 
         [0045]    It is to be understood that although the above described possible inspection algorithm activates the output as soon as a defect is detected, in an alternative embodiment the inspection algorithm may wait until all defects have been detected and activate the output according to a decision making process based on the presence of specific combinations of defects or may even determine a path amongst many paths depending on the detected combinations of defects. 
       Image Processing 
       [0046]    An possible image processing algorithm that may be used, at block  204 , to process the digital image  200  to create the high contrast image  201  is depicted by the flow diagram shown in  FIG. 4 . The steps of the algorithm are indicated by blocks  302  to  308 . 
         [0047]    The algorithm starts at block  302  by applying an enhancement filter to the digital image  200  using, for example, the “image domain tool” with an RGB gain of 3.6 for the Red and 0 for both the Blue and the Green. This is to accentuate the pixels belonging to the carcass  103  and may vary depending on the type of carcass being inspected. In this specific example the type of carcass being inspected is poultry. 
         [0048]    At block  304 , the filtered image is transformed into a grayscale image and, at block  306 , a threshold filter is applied so as to clarify the image. The “blob” tool with a luminosity threshold of 41% may be used for this purpose. Of course, this threshold may be adjusted depending on the type of carcass, lighting conditions, etc. 
         [0049]    Then, at block  308 , an unrelated structure filter may be applied in order to retain a representation of the present carcass and exclude any wing parts from neighboring carcasses which may be in the field of view of the digital camera  114   a . For that purpose the “minimum size” tool with a value of 50,000 pixels may be applied. 
       Carcass Detection 
       [0050]    The detection of a carcass, which is performed at block  206 , may be performed by detecting the presence of at least 50,000 pixels in the high contrast image  201  using, for example, the “pixel counter” tool. This allows for the elimination of part of a wing from a neighboring carcass which may have been in the field of view of the digital camera  114   a.    
       Leg Detection 
       [0051]    The detection of the presence of one or two legs, which is performed at block  208 , may be performed by detecting if there are two or four contrast changes along a line placed across where the legs should be present on the high contrast image  201 .  FIG. 6  illustrates a schematic view of the contour  201   a  of the high contrast image  201  on which is placed a line  220  at the 590 th  pixel in the x axis, the origin of the coordinates being at the bottom right corner of the image. The algorithm uses, for example, the “position along line” tool to detect the contrast changes  222 ,  224 ,  226  and  228  along line  220 . In this specific case two legs are detected. If only one leg was present the algorithm would have detected only two contrast changes instead of four. 
         [0052]    Alternatively, the detection of the presence of one or two legs may be performed by detecting if there is at least one contrast change along each of two lines (not shown) placed across where each of the legs should be present on the high contrast image  201 . 
       Wing Detection 
       [0053]    A possible algorithm for the detection of the presence of the wings that may be used at block  210  is depicted by the flow diagram shown in  FIGS. 7A  and  7 B with reference to  FIG. 10 . The steps of the algorithm are indicated by blocks  312  to  342 . 
         [0054]    At block  312  the algorithm starts by setting the side of the carcass  103  to be inspected to the right side. Then, at block  314 , the farthest point (pixel)  230  from the center of the carcass, e.g. the pixel with the lowest y coordinate value, is identified, as shown in  FIG. 10 . The “line fit” tool may be used to determine the farthest point  230  along the contour  201   a  of the high contrast image  200 . It is to be understood that if it were the left side of the carcass that was being inspected that the farthest point would be the one with the highest y axis coordinate value. 
         [0055]    At block  316 , the algorithm identifies an upper reference point  240 . The algorithm performed to identify the upper reference point  240  will be detailed further below. Using the upper reference point  240  identified at block  316 , the algorithm, at block  318 , sets detection windows anchor points  242   a  and  244   a , as shown in  FIG. 11 . The anchor points  242   a ,  244   a  may be set using predetermined x and y coordinate offsets. For example, assuming that the upper reference point  240  has coordinates ( 295 ,  165 ), the first offset may be ( 220 ,  120 ) and the second offset ( 170 ,  160 ) resulting in anchor point  242   a  and  244   a , having coordinates ( 75 ,  45 ) and ( 125 ,  105 ), respectively. It is to be understood that these offset values may vary depending on the application and the type of carcass being inspected. 
         [0056]    At block  320 , the detection windows  242  and  244  are positioned using anchor points  242   a  and  244   a , respectively, as the bottom right corners of rectangles having dimensions of 240 pixels in the x axis, 110 pixels in the y axis, and 185 pixels in the x axis, 40 pixels in the y axis, respectively. It is to be understood that these dimensions may vary depending on the application and the type of carcass being inspected. 
         [0057]    At block  322 , the length of the wing is estimated by computing the distance between the farthest point  230  and the upper reference point  240 . It is to be understood that any other suitable reference points along the contour  201   a  which are representative of the side of the carcass  103  may be used for the approximation. Then, at block  324  the algorithm verifies if the estimated wing length is greater than 33 pixels. If not, the algorithm goes to block  326  were it registers that a wing is missing and then exits. 
         [0058]    At block  328 , the width of the wing is estimated by computing the distance  250  between the two farthest points  246 ,  248  along the contour  201   a  within detection window  244 . The “measure an area” tool may be used to determine distance  250 . Then, at block  330  the algorithm verifies if the estimated wing width is greater than 105 pixels. If not, the algorithm goes to block  338 . 
         [0059]    At block  332 , the number of pixels in the wing is estimated by computing the number of pixels within detection window  242 . The “pixel counter” tool may be used to determine number of pixels within detection window  242 . Then, at block  334  the algorithm verifies if the estimated number of pixels in the wing is greater than 4,845 pixels. If not, the algorithm goes to block  336  were it registers that a wing drum is present instead of a wing and then exits. 
         [0060]    At block  338 , the algorithm verifies if the left side of the high contrast image  201  has been inspected. If not, at block  340 , the algorithm sets the side of the carcass  103  to be inspected to the left side and goes back to block  314 . If both sides of the carcass  103  have been inspected, the algorithm exits. 
         [0061]    It is to be understood that the example algorithm described above exits as soon as a defect is detected but that in alternative embodiment the algorithm may wait until all defects have been detected before exiting. 
       Identification of the Upper Reference Point 
       [0062]    A first embodiment of a possible algorithm for the identification of the upper reference point that may be used at block  316  is depicted by the flow diagram shown in  FIG. 8  with reference to  FIG. 10 . The steps of the algorithm are indicated by blocks  502  to  520 . 
         [0063]    At block  502  the algorithm starts by setting an initial point  236 . This initial point  236  is selected as being the point having the highest x coordinate amongst the point located on a path  234  along the contour  201   a  of the high contrast image  200  comprised in window  232 . The anchor point  232   a  of window  232  is has coordinates ( 120 ,  5 ) and the window  232  is a rectangle having dimensions of 270 pixels in the x axis, 290 pixels in the y axis. 
         [0064]    At block  504 , the algorithm verifies if the difference in the y axis coordinates (D(y)) of the first eight points along path  234  is less than 10. If so, the algorithm proceeds to block  506  where the coordinates of the reference point of the previous image are returned and then exits. This step is to help eliminate cases where two adjacent carcasses are in contact or very close proximity. The “fine fit” tool may be used to determine the first eight points along path  234 . 
         [0065]    At block  508 , the next point in the decreasing x axis direction along path  234  is determined. For example, the next point after initial point  236  is point  237 , and after point  237  is point  238 . The “line fit” tool may be used to determine the next point along path  234 . 
         [0066]    Then, at block  510 , the algorithm verifies if the difference in the y axis coordinates (D(y)) of the last two points is higher than 6 and the difference in the x axis coordinates as a ratio of the total x axis distance of path  234  (Dp(x)) of the last point, i.e. the difference in the x axis coordinates between the last point and point  236  divided by the difference in x axis coordinates between point  236   d  and  236 , is greater than 25% (point  236   a ), which would indicate that the algorithm has reached the beginning of the wing. If so, the algorithm proceeds to block  512  where the coordinates of the previous point are returned. 
         [0067]    At block  514 , the algorithm verifies if the difference in the y axis coordinates (D(y)) of the last three points is higher than 6 and the difference in the x axis coordinates as a ratio of the total x axis distance of path  234  (Dp(x)) of the last point is greater than 25% (point  236   a ) and less or equal to 75% (point  236   c ). If so, the algorithm proceeds to block  516  where the coordinates of the last point are returned. 
         [0068]    Finally, at block  518 , the algorithm verifies if the difference in the x axis coordinates as a ratio of the total x axis distance of path  234  (Dp(x)) of the last point is equal to 100% (point  236   d ), i.e. the algorithm has reached the end of path  234 . If so, the algorithm proceeds to block  520  where the coordinates of the last point are returned. If not, the algorithm goes back to block  508  to find the next point along path  234 . 
         [0069]    Basically, the algorithm travels along path  234  until it detects the beginning of the wing, in which case it returns the coordinates of the point just before the start of the wing or the point  236   d  at the end of path  234  if the beginning of the wing has not been detected yet. For example, of the algorithm had just determined point  240  as the next point, it would verify if the difference in the y axis coordinates of point  240  and point  239  is higher than 6 and the difference in the x axis coordinates as a ratio of the total x axis distance of path  234  (Dp(x)) of point  240  is greater than 25%. If so, then the returned coordinates would be those of point  239 . Otherwise, the algorithm would verify if the difference in the y axis coordinates (D(y)) of the last three points is higher than 6 and the difference in the x axis coordinates as a ratio of the total x axis distance of path  234  (Dp(x)) of point  240  is greater than 25% (point  236   a ) and less or equal to 75% (point  236   c ), if so, the algorithm would return the coordinates of point  240 . Then, the algorithm would verify if the difference in the x axis coordinates as a ratio of the total x axis distance of path  234  (Dp(x)) of point  240  is equal to 100% (point 236 d ). If so, the algorithm would return the coordinates of point  240 , if not, it would go back to block  508  in order to determine the next point along path  234 . 
         [0070]    A second embodiment of a possible algorithm for the identification of the upper reference point that may be used at block  316  is depicted by the flow diagram shown in  FIG. 9  with reference to  FIG. 10 . The steps of the algorithm are indicated by blocks  602  to  616 . 
         [0071]    At block  602  the algorithm starts by setting an initial point  236 . This initial point  236  is selected as being the point having the highest x coordinate amongst the point located on a path  234  along the contour  201   a  of the high contrast image  200  comprised in window  232 . The anchor point  232   a of window  232  is has coordinates ( 120 ,  5 ) and the window  232  is a rectangle having dimensions of 270 pixels in the x axis, 290 pixels in the y axis. 
         [0072]    At block  604  a counter is set to 1 following which, at block  606 , the next point in the decreasing x axis direction along path  234  is determined. For example, the next point after initial point  236  is point  237 , and after point  237  is point  238 . The “line fit” tool may be used to determine the next point along path  234 . Once the next point is determined, at block  608 , the counter is increased by 1. 
         [0073]    Then, at block  610 , the algorithm verifies if the difference in the y axis coordinates (D(y)) of the last two points is higher than 6, which would indicate that the algorithm has reached the beginning of the wing. If so, the algorithm proceeds to block  612  where the coordinates of the previous point is returned. However, if the difference in the y axis coordinates (D(y)) of the last two points is not higher than 6, the algorithm proceeds to block  614  where it verifies if the counter has reached 50. If so, the algorithm proceeds to block  616  where the coordinates of the last point is returned. If not, the algorithm goes back to block  606  to find the next point along path  234 . 
         [0074]    Basically, the algorithm travels along path  234  until it detects the beginning of the wing, in which case it returns the coordinates of the point just before the start of the wing or the value of the 50 th  point along the path if the beginning of the wing has not been detected yet. For example, if the algorithm had just determined point  240  as the next point, it would verify if the difference in the y axis coordinates (D(y)) of point  240  and point  239  is higher than 6. It is higher than 6 then the returned coordinates would be those of point  239 . On the other hand, if the difference in y axis coordinates was not higher than 6 then the algorithm would verify if point  240  was the 50 th  point along path  234 , by verifying if the counter had reached 50. If so, the algorithm would return the coordinates of point  240 , if not, it would go back to block  606  in order to determine the next point along path  234 . 
       Hole Detection 
       [0075]    A possible algorithm for the detection of the presence of a hole in one of the legs that may be used at block  212  is depicted by the flow diagram shown in  FIG. 12  with reference to  FIG. 13 . The steps of the algorithm are indicated by blocks  352  to  370 . 
         [0076]    At block  352  the algorithm starts by setting the side of the carcass  103  to be inspected to the right side. Then, at block  354 , a lower reference point  254  is determined. To determine the coordinates of the lower reference point  254  a line  252  is place over the contour  201   a  of the high contrast image  201  at the 370 th  pixel in the x axis and the algorithm uses, for example, the “position along line” tool to detect the contrast changes  254  along line  252 . 
         [0077]    At block  356 , using the lower reference point  254  identified at block  354 , the algorithm sets a detection window  256  anchor point  256   a.  The anchor point  256   a  is set using predetermined x and y coordinate offsets. For example, assuming that the lower reference point  254  has coordinates ( 370 ,  160 ), the offsets may be ( 35 ,  55 ), resulting in anchor point  256   a  having coordinates ( 335 ,  105 ). It is to be understood that these offset values may vary depending on the application and the type of carcass being inspected. 
         [0078]    At block  358 , the detection window  256  is positioned using anchor point  256   a  as the bottom right corner of a rectangle having dimensions of 200 pixels in the x axis, 110 pixels in the y axis. It is to be understood that these dimensions may vary depending on the application and the type of carcass being inspected. 
         [0079]    At block  360 , the number of consecutive black pixels within detection window  256 , but not touching the sides of the detection window  256 , is computed. The “pixel counter” tool may be used to determine number of consecutive black pixels within detection window  242 . In order to better detect the presence of holes, the “blob” tool may be used with a threshold of 81% before computing the number of consecutive black pixels. This as for effect to set to black any lighter intensity pixels that may represent damage skin around the holes which may isolate black pixels from one another. 
         [0080]    Then, at block  364  the algorithm verifies if the computed number of consecutive black pixels is lower is lower than 605 pixels. If not, the algorithm goes to block  366  were it registers that a hole is present in a leg and exits. 
         [0081]    Finally, at block  370 , the algorithm verities if the left side of the high contrast image  201   a  has been inspected. If not, the algorithm sets the side of the carcass  103  to be inspected to the left side and goes back to block  354 . If both sides of the carcass  103  have been inspected, the algorithm exits. 
       Skin Condition Detection 
       [0082]    A possible algorithm for the detection of the presence of a skin condition that may be used at block  214  is depicted by the flow diagram shown in  FIGS. 14A and 14B  with reference to  FIG. 15 . The steps of the algorithm are indicated by blocks  372  to  406 . 
         [0083]    At block  372  the algorithm starts by setting a center detection window  260  anchor point  260   a  using the upper reference point  240  identified at block  316  of the wing detection algorithm. The anchor point  260   a  is set using predetermined x and y coordinate offsets. For example, assuming that the lower reference point  240  has coordinates ( 295 ,  165 ), the offsets may be ( 65 ,  25 ), resulting in anchor point  260   a  having coordinates ( 230 ,  190 ). It is to be understood that these offset values may vary depending on the application and the type of carcass being inspected. 
         [0084]    At block  374 , the center detection window  260  is positioned using anchor point  260   a  as the bottom right corner of a rectangle having dimensions of 70 pixels in the x axis, 110 pixels in the y axis, it is to be understood that these dimensions may vary depending on the application and the type of carcass being inspected. 
         [0085]    At block  376 , the algorithm computes the number of pixels with a color corresponding to a skin condition within center detection window  260  placed on the digital image  200 . The “pixel counter” tool, in conjunction with a table of RGB values corresponding to colors associated with the skin condition, may be used to determine number of pixels with a color corresponding to the skin condition within center detection window  260 . 
         [0086]    Then, at block  378 , the algorithm verifies if the number of pixels with a color corresponding to the skin condition is lower than 250 pixels. If not, the algorithm goes to block  380  were it registers that the skin condition is present and exits. 
         [0087]    At block  382 , the algorithm sets the side of the carcass  103  to be inspected to the right side. Then, at block  384 , using the upper reference point  240  identified at block  316 , the algorithm sets an upper side detection window  258  anchor point  258   a.  The anchor point  258   a  is set using predetermined x and y coordinate offsets. For example, assuming that the lower reference point  240  has coordinates ( 295 ,  165 ), the offsets may be ( 60 ,  15 ), resulting in anchor point  258   a  having coordinates ( 235 ,  180 ). It is to be understood that these offset values may vary depending on the application and the type of carcass being inspected. 
         [0088]    At block  386 , the upper side detection window  258  is positioned using anchor point  258   a  as the center of an ellipse having dimensions of 120 pixels in the x axis, 25 pixels in the y axis. It is to be understood that these dimensions may vary depending on the application and the type of carcass being inspected. 
         [0089]    At block  388 , the algorithm computes the number of pixels with a color corresponding to the skin condition within upper side detection window  258  placed on the digital image  200 . The “pixel counter” tool, in conjunction with a table of RGB values corresponding to colors associated with the skin condition, may be used to determine number of pixels with a color corresponding to the skin condition within upper side detection window  258 . 
         [0090]    Then, at block  390 , the algorithm verifies if the number of pixels with a color corresponding to the skin condition is lower than 330 pixels. If not, the algorithm goes to block  392  were it registers that the skin condition is present and exits. 
         [0091]    At block  394 , the algorithm sets a lower side detection window  262  anchor point  262   a  using the lower reference point  254  identified at block  354  of the hole detection algorithm. The anchor point  262   a  is set using predetermined x and y coordinate offsets. For example, assuming that the lower reference point  254  has coordinates ( 370 ,  160 ), the offsets may be ( 100 ,  15 ), resulting in anchor point  262   a  having coordinates ( 270 ,  175 ). It is to be understood that these offset values may vary depending on the application and the type of carcass being inspected. 
         [0092]    At block  396 , the lower side detection window  262  is positioned using anchor point  262   a  as the bottom right corner of a rectangle having dimensions of 165 pixels in the x axis, 75 pixels in the y axis. It is to be understood that these dimensions may vary depending on the application and the type of carcass being inspected. 
         [0093]    At block  398 , the algorithm computes the number of pixels with a color corresponding to the skin condition within center detection window  262  placed on the digital image  200 . The “pixel counter” tool, in conjunction with a table of RGB values corresponding to colors associated with the skin condition, may be used to determine number of pixels with a color corresponding to the skin condition within center detection window  262 . 
         [0094]    Then, at block  400 , the algorithm verifies if the number of pixels with a color corresponding to the skin condition is lower than 353 pixels. If not, the algorithm goes to block  402  were it registers that the skin condition is present and exits. 
         [0095]    At block  404 , the algorithm verifies if the left side of the high contrast image  201  has been inspected. If not, at block  406 , the algorithm sets the side of the carcass  103  to be inspected to the left side and goes back to block  384 . If both sides of the carcass  103  have been inspected, the algorithm exits. 
         [0096]    It is to be understood that various skin conditions may be detected, each skin condition having a table of RGB values corresponding to colors associated with the skin condition. Examples of skin conditions may be the presence of flesh, i.e. areas where the skin is missing, or redness of the skin. 
       Presence of Flesh 
       [0097]    RGB values corresponding to colors associated with the presence of flesh may be predetermined by taking digit are images of flesh and registering the ranges of RGB values within a table. Table 1 gives an example of RGB values that may be used for poultry breast flesh colors It is to be understood that the range of flesh color RGB values may vary with the type carcass, lighting conditions, body part, etc. 
         [0000]    
       
         
               
             
               
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 RGB values for poultry breast flesh colors 
               
             
          
           
               
                 Color 
                   
                   
                   
               
               
                 number 
                 Red component 
                 Green component 
                 Blue component 
               
               
                   
               
             
          
           
               
                 1 
                 28.2 to 34.1 
                 7.8 to 10.6 
                 6.3 to 10.6 
               
               
                 2 
                 25.1 to 34.1 
                 6.3 to 9.0 
                 6.3 to 9.0 
               
               
                 3 
                 28.2 to 37.3 
                 6.3 to 9.0 
                 6.3 to 12.2 
               
               
                 4 
                 28.2 to 31.0 
                 9.4 to 12.2 
                 0.0 to 5.9 
               
               
                 5 
                 28.2 to 34.1 
                 9.4 to 12.2 
                 0.0 to 5.9 
               
               
                 6 
                 31.4 to 34.1 
                 12.5 to 13.7 
                 6.3 to 9.0 
               
               
                 7 
                 31.4 to 34.1 
                 9.4 to 12.2 
                 6.3 to 9.0 
               
               
                 8 
                 25.1 to 27.8 
                 7.8 to 9.0 
                 0.0 to 5.9 
               
               
                 9 
                 25.1 to 27.8 
                 6.3 to 7.5 
                 0.0 to 5.9 
               
               
                 10 
                 31.4 to 34.1 
                 0.0 to 9.0 
                 3.1 to 9.0 
               
               
                 11 
                 31.4 to 37.3 
                 11.0 to 12.2 
                 3.1 to 9.0 
               
               
                 12 
                 37.6 to 43.5 
                 9.4 to 12.2 
                 0.0 to 5.9 
               
               
                 13 
                 40.8 to 43.5 
                 14.1 to 18.4 
                 3.1 to 9.0 
               
               
                 14 
                 34.5 to 43.5 
                 3.1 to 7.5 
                 0.0 to 9.0 
               
               
                 15 
                 43.9 to 49.8 
                 12.5 to 16.9 
                 3.1 to 12.2 
               
               
                 16 
                 40.8 to 46.7 
                 9.4 to 12.2 
                 0.0 to 9.0 
               
               
                 17 
                 15.7 to 21.6 
                 6.3 to 10.6 
                 0.0 to 9.0 
               
               
                   
               
             
          
         
       
     
       Redness of the Skin 
       [0098]    RGB values corresponding to colors associated redness of the skin may be predetermined by taking digital images of redness and registering the ranges of RGB values within a table. Table 2 gives an example of RGB values that may be used for poultry breast redness colors. It is to be understood that the range of redness color RGB values may vary with the type carcass, lighting conditions, body part, etc. 
         [0000]    
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 RGB values for poultry breast redness colors 
               
             
          
           
               
                 Color 
                   
                   
                   
               
               
                 number 
                 Red component 
                 Green component 
                 Blue component 
               
               
                   
               
               
                 1 
                 31.4 to 37.3 
                 11.0 to 12.2 
                 3.1 to 9.0 
               
               
                 2 
                 37.6 to 43.5 
                 9.4 to 12.2 
                 0.0 to 5.9 
               
               
                 3 
                 40.8 to 43.5 
                 14.1 to 18.4 
                 3.1 to 9.0 
               
               
                 4 
                 34.5 to 43.5 
                 3.1 to 7.5 
                 0.0 to 9.0 
               
               
                 5 
                 43.9 to 49.8 
                 12.5 to 16.9 
                 3.1 to 12.2 
               
               
                 6 
                 40.8 to 46.7 
                 9.4 to 12.2 
                 0.0 to 9.0 
               
               
                 7 
                 15.7 to 21.6 
                 6.3 to 10.6 
                 0.0 to 9.0 
               
               
                   
               
             
          
         
       
     
         [0099]    It is to be understood that the various values for the pixel thresholds and windows dimensions are based on the size of the digital image, which is 640 by 480 pixels in the example embodiment, the type of carcass being inspected and the specific inspection criteria. The specified values are meant as examples only and other values may be used depending on the application and quality levels. 
         [0100]    Although the present invention has been described by way of a particular embodiment and examples thereof, it should be noted that it will be apparent to persons skilled in the an that modifications may be applied to the present particular embodiment without departing from the scope of the present invention.

Technology Classification (CPC): 0