Abstract:
A food product checking system for at least one of identification and grading of food products packed in a tray is provided, comprising a tray positioning area, an illumination device for illuminating the food product with white light, at least one camera for taking images of the illuminated food product, and an evaluation device for image processing of taken images, wherein the evaluation device is adapted to perform a color analysis of taken images.

Description:
The present invention is drawn to a food product checking system for at least one of identification and grading of food products packed in a tray. 
     Further, the invention is related to a method for identifying and grading food products packaged in a tray. 
     BACKGROUND OF THE INVENTION 
     It is desirable to have a food product checking system available which can automatically identify and grade food products. Such a system is usable for quality assessment of food products. It is further usable for labeling food products automatically. 
     WO 02/27281 A1 discloses a weighing system for weighing articles conveyed on a conveyor comprising at least a first and second belt unit, the weighing system comprising a first and second weighing cell supporting the first and second belt unit, respectively, and adapted to provide data of the weight of one or more articles being supported by said belt units, and a control system with detecting means for providing data of at least the location of one or more edges of the article on the conveyor, a processor unit for processing data obtained from said detecting means and weight cells and for determining at least a weight of the article(s) and storing said determined weight. 
     WO 98/42196 A1 discloses an apparatus for removing the ribs portion from the belly of an animal flank, comprising transport means, image sensing means, means for generating surface profile data characterizing the flank top surface, means for generating ribs thickness data for the ribs, processor means, robot means provided with a cutting means and controller means operable to produce relative movement between the cutting means and the flank according to programmed cut data. 
     U.S. Pat. No. 6,133,948 A discloses an automated identification system comprising a first classification station which uses two laser sources and video imaging techniques to identify and distinguish between similar items addressed, for example, wooded cabinet doors and drawer fronts. 
     SUMMARY OF THE INVENTION 
     In accordance with the present invention, a food product checking system for at least one of identification and grading of food products packed in a tray is provided, comprising a tray positioning area, an illumination device for illuminating the food product with white light, at least one camera for taking images of the illuminated food product, and an evaluation device for image processing of taken images, wherein the evaluation device is adapted to perform the color analysis of taken images. 
     In accordance with the present invention, the food product (and also the tray) is illuminated by white light. Accordingly, the tray positioning area and thereby the food product and the tray can be illuminated by white light in a broad spectrum. The taken images can be color analyzed in a simple manner. It is simple to achieve a high color resolution and thereby investigate the food product in a definite manner. 
     The white light contains light of other colors, like red light, green light and blue light. The color analysis can be performed by resolving different colors and using the different colors to determine features of the food product and of the packaging of the food product. 
     For example, in accordance with the present invention, the contours of the food product can be distinguished from the tray in a simple manner. It is also possible to determine in a simple manner inclusions in a seal. 
     Also, it is possible to analyze a laser line across the food product and the tray in a simple manner when the corresponding light line has a definite color like green. 
     In particular, the white light is a superposition of red light, green light and blue light. (Other colored light can be included.) Thus, the color analysis can be performed with regard to red, green and blue. Illustratively, it is, for example, possible to distinguish features of meat food products. 
     In one embodiment, the illumination device comprises a source for white light. For example, the illumination device comprises one or more broadband light emitting diodes. 
     It is also possible that the illumination device comprises sources for red, green and blue light. Illustratively, the white light is generated as superposition of the light of different sources. 
     It is advantageous when a reflection area is provided from which white light is directed towards the tray positioning area. The reflection area is a kind of source for white light. For example, the reflection area is formed by plastic panels reflecting light down onto the tray positioning area. Thus, glare on a cover can be minimized. 
     In particular, the light of the sources for red, green and blue light is directed towards the reflection area and is in particular reflected towards a reflection area. 
     It is advantageous when at least one light emitting diode is provided as a light source. Light emitting diodes have comparatively low energy consumption and can be arranged in a compact way. Further, the heat production of light emitting diodes is comparatively low. It is possible that one or more broadband white light emitting diodes are provided or that one or more light emitting diodes for different colors like red, green and blue are provided. 
     It is advantageous when an enclosure is provided for the tray positioning area. Thus, the food product (and a tray) can be illuminated in a defined way minimizing the influence of disturbance light. 
     It is expedient for the enclosure to have openings allowing a through put of trays. Thus, for example, the trays can be put through a corresponding vision station via a conveyor. 
     It is particularly advantageous when, within the vision range of the at least one camera, color reference fields are arranged. These color reference fields, which are, for example, formed by corresponding red, green and blue chips, serve as reference colors. The at least one camera can monitor the light level and color balance. It is possible to adjust automatically the settings of the at least one camera when needed. Accordingly, a high reliability of the system can be reached. 
     It is advantageous when at least one color reference field for the color white is provided. It is then possible to balance a camera on the corresponding white target of this reference field or reference fields and test for example comparing white versus red in a red plane, white versus green in a green plane and so on. 
     The color reference fields are arranged advantageously at or close to the tray positioning area. Thus, reference fields are positioned in the vision field of the camera. 
     It is expedient when the at least one evaluation device is adapted to analyze an image with regard to the positions of the colors red, green and blue. For example, it is possible to discern different color areas in a taken image and thereby analyze the food product with regard to different areas. For example for a meat food product, bone areas, fat areas and lean areas can be distinguished. 
     Further, it is advantageous when the evaluation device is adapted to identify the contour or shape of the food product in the tray and separate the food product from the tray. Thus, the food product analysis can be restricted to the image of the food product. Further, the contour or shape of a food product is in itself an information useful for identifying the food product. Moreover, the shape information for a tray could be a useful information. 
     If the system comprises a laser device which is adapted to produce at least one laser generated light line across a tray, further evaluation steps (evaluation modules) can be performed. For example, via the laser line it is possible to determine the height of a tray, the dome height of a film cover and it is possible to check whether intrusions are under a cover and, in particular, under a seal area of the cover. Further, it is possible via analyzing the shape of the laser generated light line across the food product in a corresponding image the food product itself. 
     It is advantageous when the laser device emits light in an angle (oblique) with regard to a plane perpendicular to the tray positioning area. Thus, the offset between a laser area on a tray extension (tray edge) with regard to a laser generated light area on the tray positioning area can be used to determine the height of the tray. Further, with a laser generated light line on a film cover it is possible to determine a film cover dome height. 
     It is particularly advantageous for checking of meat food products if the laser device generates green light. The evaluation device then can easily determine the laser light area in an image. For other type of food products other laser light colors might be appropriate. 
     In particular, the evaluation device is adapted to analyze a laser generated light line with regard to segmentation. The segmentation of the laser light line across a food product can give valuable information with regard to the food product. 
     It can be provided that at least one of the number of segments, length of segments, length distribution of segments, angle of segments, angle distribution of segments and offset between segments is analyzed. Also, the texture of the food product around a laser generated light line can be analyzed. 
     Further, at least one of the shape of the food product, shape of areas of the food product, distribution of shape of areas of the food product, size of areas of the food product, distribution of size of areas of the food product can be analyzed by the evaluation device. 
     With the food product checking system in accordance with the present invention, also trays that are sealed by a see-through lid like a film lid can be analyzed. 
     For example, the evaluation device evaluates a laser generated light line caused by light reflection on said lid. With this light line compared to a laser generated light area on a tray extension, the dome height of the cover can be determined. 
     In accordance with the present invention it is possible to determine via the evaluation device the height of the tray by comparing the offset of light reflected from the tray positioning area or an area close to the tray positioning area to the laser generated light line on a tray extension. 
     Further, it is possible to analyze in accordance with the present invention a tray seal area with regard to inclusions. The tray seal area has, if the tray has a certain color, the same color. If there is a color change in this area, an inclusion must be present. 
     It can be advantageous when the system in accordance with the present invention comprises a first camera and at least one second camera for scanning a laser generated light area. With a second camera, for example, the sealing can be checked with regard to inclusions. With a second camera, it is for example possible to check the seal area continuously. 
     The system may comprise a conveyor for transporting trays. Accordingly, a high checking rate can be achieved. 
     It is advantageous when the evaluation device comprises a data storage for stored product identification data and/or product grading data. Such, the evaluation device can calculate for performed analysis modules numerical values and compare them with known stored data for known food products. Thus, it is possible to identify and grade food products. 
     Provision can be made for a learning section of the evaluation device. Accordingly, new products can be introduced. A new product can be run through the system and the corresponding data can be taken and, if the food product is found to be unique compared to the stored food products, the corresponding data can be stored. 
     Further, in accordance with the present invention a method for identifying and grading food products packaged in a tray is provided, which comprises evaluating the shape of the food product in the tray, and analyzing the food product with regard to different areas in the food product, wherein for analyzing the food product multiple analysis modules are performed based on different analysis criteria, each analysis module providing an output, and wherein said outputs are compared to stored food product data. 
     Thus, food products can be identified and graded in an automatic manner with high reliability. 
     For example, at least five modules are performed. In one embodiment, ten to fifteen (or more) modules are performed providing a corresponding number of criteria. (The number of criteria can be larger than the number of modules performed.) It is then possible to identify food products and/or grade food products with a top-down method. In a top-down method, non-matching food products are eliminated until matching is reached. 
     In particular, the outputs are numerical values provided by an evaluation device. The evaluation device has reached these numerical output values in particular by image processing. 
     For example, the analysis steps comprise a color analysis of the food product and a surface analysis of the food product. 
     The analysis steps are, in particular, based on optical data. In particular, the optical data are based on imaging data processing of images taken by at least one camera. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic view of an embodiment of a food product checking system in accordance with the present invention; 
         FIG. 2  is a cross section along the line  2 - 2  of  FIG. 1  of a vision station; 
         FIG. 3  is a view on a positioning area of the vision station of  FIG. 2 ; 
         FIG. 4  is a schematic cross section of a sealed tray (tray with cover lid) including a food product; 
         FIG. 5  is a schematic flow chart illustrating an embodiment of food product identification and food product grading; 
         FIG. 6  is a schematic diagram for illustrating a color analysis; the diagram has as axes the colors red, green and blue and an analyzed food product is pictured as points; 
         FIG. 7  shows an image of packaged meat during an analyzing step; the original image has blue, red and green areas. It is possible to distinguish between lean areas, fat areas and laser generated light areas; 
         FIGS. 8(   a ) to ( e ) show different samples of food products and a laser generated light line; and 
         FIG. 9  shows schematically a food product and different laser generated light lines allowing a packaging analysis. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     An embodiment of a food product checking system, which is shown in  FIG. 1  and designated as  10 , comprises a vision station  12  for performing optical checks on food products. The food product checking system  10  further comprises a weighing station  14  for weighing food products (together with their packages). The vision station  12  and the weighing station  14  can be connected in such a way that during optical inspection of food products a weighing can be performed or they can be separated from each other with the weighing station  14  following sequentially the vision station  12 , as shown in  FIG. 1 . 
     The vision station  12  comprises a stand  16 . On this stand  16 , a conveyor belt  18  is guided for transporting food product goods through the vision station  12  (to the weighing station  14 ). The conveyor belt  18  is driven by a motor  20  that is fixed to the stand  16 . 
     Further, rolls  22   a ,  22   b  are fixed to the stand  16  for guiding the conveyor belt  18 . 
     The vision station  12  comprises an enclosure  24  that is arranged on the stand  16 . Within the enclosure  24 , food products are illuminated, as described below. 
     The food products to be checked are packaged in trays as described further below. The vision station  12  comprises a tray positioning area  26  ( FIGS. 2 ,  3 ) on which trays with food products are to be positioned during examination. 
     The examination of the food products can be performed with still standing conveyor belt  18  or continuously with moving conveyor belt  18 . In the latter case, the tray positioning area  26  can be an area moving together with the conveyor belt  18 . 
     The weighing station  14 , which comprises scales  28 , is arranged in such a way that the weight of the tray-food product-entity can be measured during or after the optical inspection thereof. 
     The enclosure  24  has openings  30   a ,  30   b  which are opposite to each other and allow a through-put of food products through the vision station  12 . 
     The food product checking system further comprises a labeling station  32  which is arranged following the weighing station  14 . The vision station  12  and the labeling station  32  are connected by a transport station for transporting food products from the vision station  12  to the labeling station  32 . At the labeling station  32 , the trays with the food product are labeled. The labels, which are for example printed labels, are provided with food product information at the labeling station  32  using the results of the vision station  12  and the weighing station  14 . Such, labels can include weight information and can include the sale price which has been calculated using the weight information determined by the weighing station  14  and the product identification information determined via the vision station  12 . 
     The labeling station  32  can, for example, comprise a labeling device  36  that puts labels on food product packages via a stamp or via airflow or via a rotating roller. 
     The food product checking system  10  further comprises a control device  38  for controlling the vision station  12 , the weighing station  14  and the labeling station  32 . The control device  38 , for example, calculates sales prices on basis of the results of the vision station  12  and the weighing station  14 . 
     Further, a control station  40  can be provided following the labeling station  32 . The control station  40  comprises, for example, (at least one) camera  42 . With this camera  42 , the control station  40  is capable of checking for label presence on food product packages and is also capable of checking for correct label position. Further, with the camera  42  it is possible to check for proper label information on printed labels when the images information of the camera  42  is accordingly analyzed by the control device  38 . (For example, the control device  38  uses OCR/OCV.) 
     The control station  40  can comprise a pusher  43  for removing packaged food products which have been recognized (for example via measuring results of the vision station  12 ) as not fulfilling certain quality standards. 
     The vision station  12  comprises a holding device  44  ( FIG. 2 ), which is positioned on the stand  16 . For example, the holding device  44  is of the gantry type. 
     The holding device comprises a bar  46 , which is positioned above the tray positioning area  26  at a distance from this tray positioning area  26 . The bar  46  holds a first camera  48  and, optionally, a second camera  50 . The vision field of the first camera  48  and the second camera  50  is the tray positioning area  26 . 
     The cameras  48  and  50  are, in particular, commercially available cameras. 
     The cameras  48  and  50  are positioned within the enclosure  24 . The holding device  44  can be arranged completely within the enclosure  24  or partially within the enclosure  24  or outside of the enclosure  24 . 
     Within the enclosure  24  light sources  52  for discrete red, green and blue light are arranged. For example, these light sources  52  are light emitting diodes. The enclosure  24  has a reflection area  54  on its walls or at least partially on its walls. For example, a reflection area  54  is formed on a side  56  of the enclosure  24  facing towards the tray positioning area  26  and lying above this tray positioning area  26 . For example, the reflection area  54  is formed by panels. The panels are, for example, made from a plastic material. 
     The light of the light sources  52  is directed towards the reflection area  54  and reflected from there towards the tray positioning area  26 . The light of the light sources  52  mixes towards the reflection area  54  and when reflected from the reflection area  54 . Such, the tray positioning area  26  is illuminated by white light. In this sense, the reflection area  54  can be seen as a source for white light illuminating the tray positioning area  26 . The light sources  52  and the reflection area  54  are formed and arranged in such a way that the tray positioning area  26  is evenly lighted. 
     It is also possible that instead of “discrete” sources for discrete colors a broadband source or broadband sources for white light are provided. In particular, one or more broadband light emitting diodes are provided. It is in principle also possible that other broadband light sources like halogen lamps can be provided. 
     On the stand  26 , a holding plate  58  is arranged. This holding plate  58  is disposed within the enclosure  24 . The conveyor belt  18  is guided over the holding plate  58 . 
     On the holding plate  58  reference fields  60   a ,  60   b ,  60   c ,  60   d  are arranged. The reference field  60   a  is a red color reference field. The reference field  60   b  is a green color reference field and the reference field  60   c  is a blue color reference field. The reference field  60   d  is a white color reference field. The colors of the reference fields correspond to the colors of the light that is emitted by the corresponding light source. Such, the color of the reference field  60   a  corresponds to the color of the light sources emitting red light, the color of the reference field  60   b  corresponds to the color of the green light emitting sources and the color of the reference field  60   c  corresponds to the blue light emitting sources. 
     The reference field  60   d  for white color allows balancing on a white target. 
     The reference fields  60   a ,  60   b ,  60   c  are arranged within the vision range of the first camera  48  (and optionally the second camera  50 ). In particular, the reference fields  60   a ,  60   b ,  60   c  are arranged at or close to the tray positioning area  26  in such a way that both the reference fields  60   a ,  60   b ,  60   c  and also food product in the tray positioning area  26  is within the vision field of the first camera  48 . 
     Via the reference fields  60   a ,  60   b ,  60   c , the light level and color balance of the cameras  48 ,  50  can be monitored. It is possible to adjust camera settings of the cameras  48  and  50  automatically when needed. 
     The holding device  44  further holds a laser device  62  which is the source of green laser light  64 . The laser device  62  generates a (green) laser generated light line  66  ( FIG. 7 ) across the packaged food product. This light line  66  is used for various analysis and evaluation steps that will be described below. 
     The laser device  62  emits (via a corresponding optic arrangement) a scanning light fan which is oblique to a plane  68  that is perpendicular to the holding plate  58  and the conveyor belt  18 . Also, the plane  68  is perpendicular to a transporting direction  70  of food products through the vision station  12 . This allows, as described below, a package cover analysis. 
     A further laser device  63  can be provided for scanning in a line substantially parallel to the transport direction  70 . Such a laser device emits a scanning light fan which is indicated in  FIG. 2  with the numeral  65 . This light fan  65  can be parallel or oblique to a plane perpendicular to the plane  68 . 
     The laser device  63  emits laser light of the same color as the laser device  62 . 
     Further laser devices can be provided emitting scanning light fans. 
     As indicated in  FIG. 4 , the food products  72  to be investigated are packaged in respective trays  74 . A tray  74  has a base portion  76  and wall portions  78   a ,  78   b . Via the base portion  76  and the wall portions  78   a ,  78   b  a reception area  80  for the food product  72  is formed. 
     The tray  74  further comprises on the wall portions  78   a ,  78   b  flange-like extensions  82   a ,  82   b  which are parallel to the base portion  76 . These extensions  82   a ,  82   b  provide seal areas  84   a ,  84   b  to which a cover  86  can be attached. The cover  86  is a see-through film-like lid which allows optical access to the food product  72  in the reception area  80 . 
     The seal  86  forms a kind of dome on the tray  74 . The cover  86  is formed in such a way that the food product  72  is sealed from the outer atmosphere. Provision can be made that under the cover  86  a protective gas is captured for protection of the food product  72 . 
     It is advantageous when the tray  74  contrasts from the food product contained in the tray. In particular, the tray  74  has a color which is different from red and white when the food product is processed meat. In particular, the tray  74  is of color black. 
     The food product checking system  10  comprises an evaluation device  88  ( FIG. 2 ) for analyzing pictures taken by the first camera  48  and the optional second camera  50 . The evaluation device  88  can be a part of the control device  38  or can be separate from the control device  38  but connected to the control device  38 . 
     The evaluation device  88  receives via corresponding signal lines  90  the pixel data (image data) taken by the cameras  48  and  50 . The evaluation device  88  comprises a data storage  92  for storing food product data. By comparing analyzed and evaluated image data with stored data a food product can be identified and/or graded. 
     Further, the evaluation device  88  comprises a learning section  94  for performing teach-processes to read in data of food products that are not yet stored. 
     The food product checking system  10  and in particular the vision station  12  functions as follows:
     A food product packaged in a tray  74  is positioned in the tray positioning area  26 . For the optical analysis of the food product, the tray can be at rest or moving. Such, it is possible to perform a monitoring with the corresponding tray at rest or continuous monitoring.   

     The light sources  52  produce, respectively, discrete red, green and blue light. The tray positioning area  26  is illuminated with broad spectrum white light from the reception area  80 . Glare on a cover  86  can be minimized via reflection of the light from the reception area  80 . 
     The camera  48  is triggered on a tray edge. The evaluation device  88  determines the dimensions of the tray. Further, via image processing the contour or shape of the food product  72  in the tray  74  are analyzed. This is indicated in  FIG. 5  with the box  96  “dimension analysis”. 
     When the contour/shape of the food product  72  is known, the evaluation device  88  can separate (in an image) the food product  72  from the tray  74 . 
       FIG. 7  shows an image example of a food product  98  (meat) that is generated by the evaluation device  88  after separating the food product  98  from the tray  74 . 
     The laser generated light line  66  is produced across the food product  72  and also on the extensions  82   a ,  82   b  on the tray  74 . The evaluation device  88  searches for the corresponding portion  100   a ,  100   b  of the light lines  66  on the extensions  82   a ,  82   b . Further, the evaluation device  88  evaluates the light line  102  outside the tray  74 . The light line on the reference fields  60   a ,  60   b ,  60   c  is, for example, evaluated. 
     Between the light line  102  and the portion  100   a ,  100   b  there is an offset d 1 , d 2  depending on the height h ( FIG. 4 ) of the tray  74 . The larger this offset, the higher the tray  74 .  FIG. 3  shows two examples: A small offset d 1  and a larger offset d 2 . The corresponding trays have different heights, whereas the tray responsible for the offset d 2  has a larger height than the other tray. The evaluation device  88  can determine the offset d 1 , d 2  via imaging processing. From this offset, the height of the tray can be determined. 
     The evaluation device  88  performs a color analysis of images provided by the first camera  48 . This is indicated in  FIG. 5  with the box  104 . For the color analysis, the pixels of the images provided by the first camera  48  are grouped in a red, green, blue color space as indicated in  FIG. 6 . With a color analysis, different areas in the food product can be distinguished. For example, if the food product is meat, bone areas, lean areas and fat areas can be distinguished. Further, with the color analysis it is possible to distinguish a laser light area for the laser generated light line  66 . This area is used for further analysis. 
       FIG. 7  shows a picture of meat following a color analysis. The original picture is a false-colored blue picture including blue areas and red areas and also a green area for the laser generated light line  66 . The image is segmented and allows to distinguish between lean areas and fat areas. 
     Further, a surface analysis is performed as indicated in  FIG. 5  via the box  106 . The surface analysis uses the laser generated light line  66 . 
     The evaluation device  88  further analyzes the laser generated light line  66  on the food product  72 . For this, the corresponding green pixels in taken images are evaluated. 
     As indicated in  FIGS. 8(   a ) to  8 ( e ), the light line  66  can be segmented depending on the food product. 
     For example, as indicated in  FIG. 8(   a ), when the food product (for example, meat) is stew meat  108 , then the corresponding laser line  110  is fragmented. Between different parts of the stew meat there are offsets. 
     If, as indicated in  FIG. 8(   b ), the food product is multiple meat cut  112 , the corresponding light line  114  is less fragmented but can have larger offsets between neighboring cut portions. 
     If the food product is a single slice  116 , as indicated in  FIG. 8(   c ), the corresponding light line  118  is not fragmented. 
     Also, via analysis of the light line it is possible to distinguish whether the food product comprises thick slices or thin slices.  FIG. 8(   d ) shows rather thin slices  120 . The light line  122  has offsets  124  indicating different slices. For small slices  120 , the offsets are smaller than for thicker slices  126 , as indicated in  FIG. 8(   e ). There, the corresponding light line  128  has offsets  130  at the transition between different slices Also, the light line  128  is less segmented than the light line  122  indicating that less slices are provided. 
     Using the color information from the color analysis and the surface analysis information, food products can be identified and/or graded. 
     It is also possible to perform a cover analysis, as indicated with the box  132  in  FIG. 5 . The cover analysis is, for example, performed before the color analysis or after the surface analysis. 
     A film-like cover  86  is responsible for a further laser generated light line  134  ( FIG. 9 ). This light line  134  is brought about by laser light reflection on the cover  86 . By comparing an offset D ( FIG. 9 ) between the light line  134  and light line portions  136   a ,  136   b  on the extensions  82   a ,  82   b  of the tray  74 , it is possible to determine the height of the film dome as indicated with HD in  FIG. 4 . Such, it is possible to determine whether the cover  86  touches the food product or not, for example, it is also possible to compare the position of a film dome (as detected by the light line  134 ) to the position of the product in its package. If the two positions coincide, the product is touched by the cover  86 . 
     Also, when analyzing the light line portions  136   a ,  136   b  on the extensions  82   a ,  82   b  it is possible to determine whether intruding material is in the seal areas  84   a ,  84   b . For example, food product parts can be between the cover  86  and the extensions  82   a ,  82   b . The evaluation device  88  analyzes via imaging processing whether the seal areas  84   a ,  84   b  have the tray color (for example, black). If another color than the tray color is found there, there must be something in a seal area under or in the cover  86 . 
     As mentioned above, the packaging analysis via the cover analysis can be performed before the color analysis. 
     In a particular embodiment, the checking procedure for food products to identify the food products and grade the food products is performed as following:
     The first camera  48  is triggered on a tray edge of a tray  74 . The evaluation device  88  searches for the light line portions on both extensions  82   a ,  82   b  in order to calculate the tray height H. Afterwards, light line  134  of the cover  86  is searched for (via image processing in the evaluation device  88  using the pixel data provided by the first camera  48 ). Using the corresponding information, the height HD of the cover dome is calculated.   

     Further, the parameter of the tray  74 , that is the extensions  82   a ,  82   b  are inspected with regard to intrusions like food product material. This is done by looking for color pixels which should not be there without an intrusion. 
     The evaluation device  88  then determines the contour or shape of the food product  82  and segments the food product into areas which are distinguished by their color. In particular, the image of the food product  72  is segmented into laser areas and lean and fat areas. 
     The image of the food product  72  within its contours is analyzed using different analysis modules. The analysis results in a numeric output for each analysis module. In a particular example, ten to fifteen (or more) different analysis modules are performed. Examples for analysis modules are: 
     The texture of the food product around a laser generated light line  66 . (With such an analysis, it is for example possible to differentiate cut meat from cubed meat.) Further examples of analysis modules are the number of light line segments, the length distribution of light line segments, the angle distribution of light line segments or the offset between light line segments. Further, the shape of the food product outline is analyzed. This could give several criteria. Also, the distribution of size of fat blobs is analyzed. This can be done by image quadrant. Another criteria is the distribution of shape of the fat blobs. This can also be done by image quadrant. 
     The output values calculated by the evaluation device  88  are used for eliminating non-matching food products. The corresponding data of the food products are stored in the data storage  92 . If a matching product identification is reached, the corresponding information is provided via the control device  38  to the labeling device  36 . 
     In an embodiment in accordance with the present invention, non-matching food products are eliminated until one matching food product is left. 
     The second camera  50 , which is optional, is for making a partial scan of only the laser generated light line area during food product traveled through the vision station  12 . For example, the second camera  50  provides image data for determining whether the cover  86  touches the food product. The evaluation device  88  can then give a corresponding signal which effects in ignoring the corresponding tray for labeling at the labeling station  32  and for rejecting the corresponding package at the discharge. 
     The second camera  50  also monitors the reference fields  60   a ,  60   b ,  60   c . The learning module  94  can be an intelligent learning module or a setup module. When feeding a new product through the vision station  12 , the corresponding output values for each of the modules mentioned above are calculated. The learning module  94  can check whether the corresponding food product overlaps with stored food product data or indicates that the new product is unique. Also, range settings for each criterion can be suggested.