CA2185354A1 - Identification and control system for processed and/or transported goods - Google Patents
Identification and control system for processed and/or transported goodsInfo
- Publication number
- CA2185354A1 CA2185354A1 CA002185354A CA2185354A CA2185354A1 CA 2185354 A1 CA2185354 A1 CA 2185354A1 CA 002185354 A CA002185354 A CA 002185354A CA 2185354 A CA2185354 A CA 2185354A CA 2185354 A1 CA2185354 A1 CA 2185354A1
- Authority
- CA
- Canada
- Prior art keywords
- fact
- picture
- stored
- processing
- stations
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Classifications
-
- A—HUMAN NECESSITIES
- A22—BUTCHERING; MEAT TREATMENT; PROCESSING POULTRY OR FISH
- A22B—SLAUGHTERING
- A22B5/00—Accessories for use during or after slaughtering
- A22B5/0064—Accessories for use during or after slaughtering for classifying or grading carcasses; for measuring back fat
- A22B5/007—Non-invasive scanning of carcasses, e.g. using image recognition, tomography, X-rays, ultrasound
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
Abstract
A process is disclosed for monitoring and/or controlling the course of an object through a predetermined series of transport and/or processing operations at various sta-tions. An electronic camera (8) at an entry station takes a digital picture of each object (4a) before it enters the course of operations. The digital picture has characteristic traits for identifying the object (4a). These character-istic traits are determined and summed up in ? characteristic code. During the course of operations, cameras (8) located at control sta-tions take digital pictures of the object (4a) for recognising and identifying the object (4). These images are analysed by an image pro-cessing programme and compared with the stored characteristic code for recognising and identifying the object (4a) before the object (4a) is signalled to the central computer.
Description
2185~54 ~d~nbf;cation and Control System for Prooessed and/or T~ansported Goods The invention relates to a process for monitoring and/or controlling a predetermined series of transport and/or processing operations by recognizing and registering the transported andJor processed goods or the transport means (objects) at different stations.
Processing, distribution and storage facilities usually have many dif~erent processing stations and transport paths. Depending on the tasks to be carried out and the final products, different objects must pass through different stations and the transport paths between them. As this happens, it is vital for the course of processing and transport steps to bç followed.
A typical example of an operation in which this problem is particularly important is a modern slaughterhouse. There are many different processing stations, e.g., entry scales, flaying stations, workplaces for removal of innards, washing and cleaning stations, meat inspection stations, stations for cutting the animalc into large and fine cuts, etc. In large part, the transport means on which the delivered slaughtered ~nirn~l~ pass through , ., . ~ ~, . - - - .
Original Document ~8~354 these stations and ehe transport paths between them are meat hooks. In the cutting zones, transport is frequently carried out on moving belts. It is necessa~y here to ensure not only that the delivered slaughtered animal, e.g., an ox or a hog, undergoes all of the correct processing steps, but also that the products obtained from a particular animal are unambiguously matched to that animal at all times.
A typical example of a distribution plant where correct transport paths within the plant are of crucial importance is a ~ook and periodical wholesaler. Each day, thousands of incoming returned items must be sorled and delivered to the appropriate warehouses. Markings that were originally found on the books or periodicals, such as bar codes and~ the like, have often been removed in the meantime or are no longer legible because o~ damage to the article.
Tke automatic monitoring and control of such a series of processing and transport operations is ~requently carried out by determining the respective locations within the plant of the different transport means.
One way to monitor the transport means is to equip these means with transmitters. A
method of this type is described in the German Document Laid Open to Public Inspection 37 11237 A1. In this instance, each transport container is equipped with a transponder. The transponder, which can be coded by a coding unit as often as desired, is electromagnetically excited in the vicinity of a transmitting antenna controlled by an inquiry system and thus emits a particular code, which in turn is received by the inquiry system.
Original Document -, 2185~5g Another common method is tO apply m~rkings or identific~tion means, e.g., bar codes, labels ~ith plain text symbols, etc., to the transport means or the transported or processed goods th~rnselves. These markings or id~rltifiers are read by suitable reading devices at the different stations.
~or exarnple, OS-l~E 32 05 189 A1 relates to a system for recognizing an orientation symbol, which serves as an aid in automatic code reading during the sorting of packages.
In all of these methods, the transport means or the transported or processed goods themselves must be specially equipped with identification means. When these ide~tification means are active means, such as transrnitters or transponders, they are subject to wear and tear. In the case of passive identification means, such as applied labels, the identification means frequently become impossible to read clearly after a certain period of time, because of dirt or partial damage; under some circumstances, they may even be read incorrectly.
The obje~ of the invention is to create a process for monitoring and/or controlling a predetermined series of transport and/or processing operations of transported and/or processed goods which does not require identification means or markings to be specially applied to the transport means or the processed or transported goods and which operates reliably and safely and can be easily installed.
This obiect is attained according to the invention in that at least a section of the object to be identified is photographed by an electronic camera. The Origin~l Document ~853'~
cha, a~ ;stic traits of the original surface and/or form of the o~ject are unambiguously derived from the digitalized photograph by an image pr~cessing prograrn and then are summed up in a characteristic code and stored and/or compared to previously stored characteristic codes for the purpose of iclentifi~tion.
The subci~im~ that follow the first claim contain advantageous embodiments and further developments of the invention.
The process on which the invention is based allows a wide variety of transported or processed goo~ or transport means, e.g., transport hooks, crates that move on conveyor belts, bottles, etc., to be identified.
Because the process accord;ng to the invention does not require markings of any sort to be applied to the objects to be identified, it is especially advantageous when there is an open circuit of objects, for example, when a plant uses no discrete tr~nsport means, such as crates, hooks or the like, that could be assigned to particular goods, or when external transport means enter the plant and subsequently leavç it again.
The characteristic codes of objects that enter the processing and transport sequence can be determined anew and stored by the system in each case, and then deleted when these objects leave the processing and transport sequence.
When changes occur in an object during processing, ~or example, when an object is divided up into multiple pieces or multiple pieces are combined to form one new piece, ()riginal Document - ~18~-~5l these changes can be determined and stored in the same ~ay.
It is also possible to sort types of objects on the basis of their characteristic traits.
Advanta~eously, the picture taken by the electronic camera is first stored in an electronic image memory of a computer in a pixel matrix.
All types of image processing routines and operations can be used on the picture stored in the pixel matrix. For example, a Laplace operation results in harder edges and greater image definition; mean value operations leads to smoothing at the expense of i~nage definition; and medi~ operations lead to smoothing while edges are maintained. Zoom operations allow particular details to be enlarged.
The pictllre of each object has characteristic traits that serve tO unarnbiguously identify the object. These traits can be determined by an image processing program and summed up in a characteristic code. Such traits include, for example, the shape and external dimensions of the object, edges, depressions, elevations, holes, scratches, labels, etc. On the basis of these traits, the unambiguous identification and recognition of objects, similar to the identification of persons on the basis of fingerprints, is possible. The position and size of the traits relatiYe to one another can be determined in the grid of the pixel matrix.
To allow different objects to be compared, the image processing program ad.~antageously standardizes ~he position of each particular picture by resorting or readdressing the pixels in the pixel Original Doc~lment ~18~35~
matrix according to predeterm;ned criteria. It is also advantageous to select an area in the position-standardized picture to be standardized by height and width, or to transform a section of the picture that includes the object to be identified or ~ predetermined part thereof to a predetermined size.
Preferably, a gray scale analysis is carried out with the standardized picture area. ~irst, the gray scale values of the individual pixels are determined and stored in a gray scale value matrix.
In addition, a gray scale histogram (c~. Fig. ~) can be created.
The gray scale values of individual pixels can be standardized relative to a pre-established value.
In this way, different light conditions can be taken into account.
The characteristic code of each particular object consists of different trait sets. Which trait sets are found and used for identification depends on the particular type of object, on the particular task (sorting known types of objects, identifying objects changed in the course of operations, etc.), on time-related information, e.g., identification speed, and on the degree of difficulty of the objects.
~;or example, the positional coordinates and dimensions of the primary picture components, i.e., object traits such as shape and size, edges, depressions, etc., can be included in a trait set (A1, A2, ...). Furthermore, it is possible to determine gray scale value distribution parameters, such as arithmetic mean v~lue, quadratic mean value, variance, half-value width, curtosis, etc., from the gray scaJe value histogram and to include these in another trait set (B1, B2, ... ).
Original Oocument - ~8 j354 In addition, a Fourier analys;s can be carried OUt with the gray scale value distribution, and the Fourier coefficients obtained from this can be included in another trait set ~C1, C2, ... ).
In a further advalltag~ous method, the picture is first transformed into a g~adient display, i which each pixel is replaced by the gradient occurring at that point. In this display, the characteristic traits of the pictured object with the strongest gradients are then determined, up to a predetermined number of traits. After this, the gradient display is converted into a display that discrimin~tes in reference tO a threshold value and stored The threshold value lies below the lowest gradient value of the previously deterrnined traits The result is a "black-white" image that constitutes the trait set in a matrix representation, wherein only the most strongly characterizing traits are present In this way, the memory required for an image can be reduced significantly. The image obtained contains only the important information, i.e, the "fingerprint" of the object in question So that it is possible for an object tO be recognized despite any slight changes that might have occurred in its "fingerprint," it is advantageous to blur the black-white images that are to be compared, i e, to make them less sharp This is ideally done by replacing, prior to producing the gradient display, the gray scale value of each pixel by the median of the gray scale values of the pixel itself and the pixels located in a given environment surrounding of the pixel.
Another option is to process the image, prior to producing the gradient display, with a blurring filter that replaces the discrete gray scale values at the individual pixels by a Gaussian distribution at a given width and Original Document ~8~354 `
from this generates a new, less sharp gray scale value image. This process step can also be carried out with the gradient display prior to discrimination relative to a threshold va~ue.
Another simple method-which, however, produces no defined blurring-is to merely set the lens somewhat out o~ focus while the pictures are being taken by the camera.
Different minimum agreement conditions (WA, WB, WC, ...), which must be ~.ceede~l during a comparison for the purpose of identification, can be established for different types of trait sets.
Advantageously, prior to entering the processing or transport sequence, the objects are first photographed by an electronic camera at an entry station; the data are stored in a central data base. All other computers at the individual observation stations, as well as a central computer, are networked with this central data base.
A processing or transport plan, which indicates what object is to pass through what station at what time, can be stored in the central computer, for exarnple.
When types of obiects are to be sorted on the basis of their (known) characteristic traits, it is also possible to place a library of object traits or even entire object pictures in the central data base of the central computer.
If new objects are created during the course of processing, the characteristic codes of these objects or the expected values ~or such characteristic codes can be s~ored in the central data base or in Original I~ocument - 218~354 the cells of a neural networl~ simulated by the central computer. The expected values for the~e new objects can include, for example, the external contours and the area encolllpassed therein from various points of view. This takes into account the fact that objects pass by the control station at di~l~ent orientations. Along ~ith data on the expected contours of a particular ob~ect, expected values related to color composition or brightness variations on the surface can also be provided.
It is also advantageous for the characteristic code of each object to include the time of registration at the entry station. Based on the stored operational plan and the times at which particular objects entered the processing or transport sequence, it is then 'possible, taking into account the transport and processing times, to calculate in advance which objects can be expected at a given control station and given time intervals with a particular probab;lity.
Then, in order to identify an obje~ at the gi~en control station, only the characteristic codes of these particular groups of objects are used initially for agreement comparison. In this way, recognition time can be significantly reduced.
The control stations signal to the central computer which of the objects has reached or passed through a certain processing station or transport path. These data can be checked in the central computer on the basis of the stored operational plan; in addition, the correct routes c~n be set for conveying the object from this point to the next processing station or transport path.
Onginal I~ocument ~1~5354 Prior to leaving the transport or processing area, tbe objects are advantageously photographed again by an electronic camera at an exit station. The camera signals the exiting objects to the central computer for control purposes. A job completion message can then be issued and the characteristic code of the object removed from the central data base. If needed, a device that provides each outgoing object with proof of identification and origin can be activated. For example, this can ~e a label printer activated by the central computer that prints an identification and origin label for each object destined for forwarding or delivery, which is then stuck onto the object.
For optimal lltili7~tion of computer capacities, it is advantageous ~or object-sign~lling in;tiators, such as light barriers or mech~nical or inductivç contact switches, to be arranged at the individual stations. When an object is located in the precise image area of the camera in question, these initiators send a release signal to the carnera, so that only in these instances are digital pictures taken and stored. In th;s way, the unnecessary analysis of pictures that contain no objects is avoided.
When a series of operations, for example, on an assembly line, must be controlled, complete control areas can be formed by combining the directly adjacent or overlapping image areas of different cameras. These areas can in turn be divided into directly adjacent or overlapping control fields. If desired, one control field can correspond to the image area of one carnera;
however, this is not absolutely necessary. As a result, it is also possible to follow the objects and their processing in a larger area.
Original Document ~18~35~
Of course, it is also possible, if desired, to divide an image area that can be seen by ~ single camera into several control fields during analysis.
Original Doc-~m~nt ~185~-5g The invention is described below in greater detail in reference to Figures 1 to 8 using the example of use in a slaughterhouse. The drawings show:
Fig. 1 Schçm~tic overview of a slaughterhouse;
Fig. lA A control station with a meat hook, in side view, and a camera directed at this with a light barrier as the release initiator for the camera;
Fig. 2 A rear view of a meat hook as seen from the camera;
Fig. 3 A pixel matrix with a sectional image of a meat hook; resolution into individual pixels is not shown;
Fig. 4 A pixel matrix with a standardized image area of the mea~ hook;
Fig. 5 A gray value scale histogram of the standardized image area;
Fig. 6 A side view of a transport path, with a suspended slaughtered animal, and a cutting belt with cameras arranged above it;
Fig. 7 A top view of the cutting belt with slaughtered anim~ls and animal parts distributed thereon;
Fig. 8 A side view of the cutting belt with a camera arranged above it and the adjacent cutti~g workplace.
Original Document 8~3~4 Figure 1 shows a schematic overview of a slaughterhouse. In the s3aughterhouse, there are a large number of different processing stations (1) and transport paths (2). The anim~ls delivered for slaughter are killed at a first station (3), suspended on a meat hook (4a) that serves as the transport means, transported to an entry scale, and then transported further to the individual processing stations (1), e.g., flaying stations, workplaces for removal of innards, washing and cleaning stations, meat inspection points, large and fine cutting stations, etc.
Prior to entering the processing sequence, e.g., a~ter the killing station (3), each meat hook (4a) with a slaughtered anim~l suspended on it is registered and assigned a job number or meat hook number A processing plan stored in a central computer registers the correct entry time and establishes which processing stations must be passed through by the meat hook with the slaughtered ~nimal suspended thereon and through which exit station the meat hook is to leave the slaughterhouse.
The following diagr~m shows an excerpt from such a processing plan:
Job No./ Entry Processing Stations Exit Stations Meat Time Hook No. V1 V2 V3 ... A1 A2 A3 ...
9:53 1 2 3 2 4 8 2 9:56 3 6 4 5 7 24 3 9:59 20 5 8 52 27 30 4 10:03 14 9 19 54 34 37 ... ... ... ... ... ... ... ...
71 11:20 34 40 20 60 37 40 72 11:27 41 47 23 67 38 45 . . .
Replacement Page (Rule 26) 2~8~54 A meat hook (4a) that has entered the sl~ughterhouse is registered at an entry station (7). At the entry station, a digital sectional picture (9~ is taken (cf. Figs. lA and 3) by a first electronic camera (8) and stored in a pixel mat~ix (11) in an electronic image memory of a computer (10).
Figure 4 shows a pixel matrix (11) con~isting of 512 x 512 pixels.
However, the photographic matrix ~11) may also include more or fewer pixels, e.g., 256 x 256 or 1024 x 1024. The sectional picture (9) of each meat hook ~4a) has ~1n~nbiguous characteristic traits identi~ying the meat hook (4a), which can be determined by means of an image processing program and summed up in a characteristic code. The characteristic code of each meat hook (4a) is stored in a central data base (5A). Characteristic traits in~lude, for example, edges, depressions, elevations, scratches, holes and the like. An unambiguous identification is possi~le on the ~asis of these characteristics.
In order to identify and recognize the particular meat hook (4a), its edges, depressions, elevations and/or holes are found in the sectional picture (9), and their position and dimensions in the grid of the pixel matrix (11) and relative to one another are determined by means of an image processlng program.
.
Positional standardization of the particular sectional picture (9) is carried out by resorting or readdressing the pixels in the grid of the pixel matrix (11) on t~le basis of predetermined criteria using an image processing prograrn. A piclure area (~A) standardized by height and width is then selected in the position-standardized sectional picture (9).
Original ~ocument 5 ~
Using the standardized pictllre area (9A), a gray scale value analysis is carried out. The gray scale values of the individual pixels are found and stored in a gray scale value matrix.
An image of this type enconlpa~es 4096 bytes. 'rhe gray scale value of eæh pixel is then replaced by the median of the gray scale values of the pixel and the pixels located in a predetermined environment surrounding the pixel. After this, the picture is transformed into a gradient image, i.e., the individual pixel value is replaced by a value corresponding to the gradient at this location. In this gradient display, the traits most strongly characteristic of the pictured object, i.e., the traits with the strongest gradients, are found, up to a predetermined number of traits. A number of c. 800 traits has proved sufficient here. ~Next, the p;cture in the gradient display is prc~essed with a (mathematic~l) blurring filter, blurring the contours of the characteristic traits. Finally, the gradient display is converted into a display that discrimin~tes in reference to a threshold value and then stored. The threshold value is lower than the lowest gradient value of the previously determined traits.
The result is ~ black-white image in which only the characterizing traits appear. The picture still has a memory requirement of 512 bytes. This "fingerprint" of the object is then stored as the trait set.
Blurring contours by ~orming the median or applying the blurring filter is done so that even if the "fingerprin~s" have changed slightly, the passing objects can still be classified.
Original l:)ocument ~85~5g Irlstead of the rather complex mathematical contour blurring, the camera lens can be set somewhat out of focus before the picture is taken. The process takes on some ~e~el of uncertainty as a result, on the other hand, however, considerable time is gained. After this, as described above in reference to the exarnple of meat hook recognition, a "black-white fingerprint" of the picture is produced as the essential trait set of the object. This picture ;s then compared bit-by-bit to stored pictures that are possible candidates for agreement, and the picture with the greatest ~greement is sought. The time needed to identify a book by this method can be less than 100 ms.
After the book has been identified, the computer issues appropriate ins~ructions, so that the book makes it way to the desired warehouse area. At the same time, the pertinentinformation is passed along to the bookkeeping department of the warehouse, etc, Before and/or after each of the processing stations (I) or transport paths (2), a digital sectional picture (9) of a particular meat hook (4a) is taken by a further electronic camera (8) at the control stations (12) and stored in a pi~zel matrix (11) in a digital image memory of a computer (10). To recognize the particular meat hook (4a), the sectional picture (9) is analyzed by an image processing program, as described above, and compared to the characteristic codes stored in the central data base (SA).
Identification is carried out essentially by means of the bit-by-bit comparison of the "black-~hite fingerprints" stored as the matrix-form trait sets. The Orig~nal Document - 21~5~
picture of the hook (4a) to be identified is compared to all other pictures of hooks ~4a) in circulation that are possible candidates, and the hook (4a) with the greatest agreçment ~s sought. A certain minimum agreement must be achieved, however. The minimurn agreement threshold can be selected depending on the requirements of the particular plant. An advantageous value is found at agreement of 60%.
Other trait sets ~e.g., time of previous or entry-side registration) are used for comparison, as needed, in order to limit the number of "black-white images" to be compared. For example, based on the processing plan and the entry times of the meat hooks (4a) into the prc~cessing sequence, and taking into account the transport and processing times, it is possible to calculate in advance which meat hooks (4a) will, with a particular probability, be at certain control or exit stations (6, 12) at particular time intervals. In order to identify a meat hook (4a) at a particular control or exit station (6, 12), only the characteristic code of this group of transport means (4) is initially used for agreement compa~ison. In this way, recognition time is significantly reduced.
After this, the centra~ computer (5) is signalled which of the meat hooks (4a) has reached or passed through the particular processing station (1) or transport path (2). On the basis of the processing pian stored in the central computer (5), a check is made to determine whether it is correct for the identified meat hook (4a) to be at the control station in question. As needed, a router (13) is set for conveying the meat hoo~ (4a) to the next processing station (1) or transport path (2).
Original I)ocument 218~54 Exit stations (6) are located at the slaughterhouse delivery ramps (14), at each of which another electronic camera (8) takes a digital sectional picture (9) of the respective meat hooks (4a). The picture is stored in an electronic image memory of a computer (10). In order to recognize the meat hook (4a), the sectional picture ~9) is analyzed by an image p,ocess;ng program and compared to the characteristic codes stored in the central data base (SA). The central computer (5) is signalled which of the meat hooks ~4a) has reached the exit station (6) in question. A check is made, in reference to the proçessing plan, to determine whether it is correct for the identified meat hook (4a) to be at the exit station (6) in question. As needed, a job connpletion message or the like is issued and the characteristic code of this meat hook (4a) is removed from the central data base (5A).
At the respective entry stations (7), control stations (12) and exit stations (6), there is an initiator, preferably a light barrier (1~, 15'), which signalizes the meat hooks (4a) and emits a release signal (AS) for the appropriate camera (8), so that a digital sectional picture (9) of the sign~li7ed meat hook (4a) is taken by the camera (8) and stored. In addition, the time of registration is signalled to the central computer (5) and can be included in the job protocol.
Instead of a light barrier (15, 15'), the initiator may also be a mechanical or inductive contact switch.
In an alternative embodiment, pictures are taken by the particular camera (3) at periodic sequential time intervals that are substantially shorter than the passage times of the meat hooks (4a) through the Ori~inal Document -control stations (12). In each case, a rough linear or columnar grid is established in order to determine whether a meat hook (4a) is contained in the picture or not. If there is a meat hook (4a) in the picture, the picture is stored for further analysis.
The central computer (~), the central data base (~A) and all of the other computers (10) are networked ~vith one another.
The picture signals Q3S) ~rom the entry, control and exit stations are sent to the central computer (5~. The routing instruction signals (WS) are established by the central computer (5).
Until re~ching the cutting zone (16), the individual slaughtered anim~ls (4b) are transported suspended on meat hooks (4a) and via transport rails (2). The process according to the invention makes it possible to know which slaughtered ~nimal~ (4b) are suspended on which meat hooks (4a).
When the cutting zone (16) is reached, the exit station (6) for meat-hook monitoring turns over the appropriate identification start address to the analysis computer (5); advantageously, the arrival or start time is stored at the same time.
The cutting zone (16) consists of a cutting belt (19) and multiple cutting workplaces (lB). The slaughtered animals (4b) are moved from the transport path (2) onto the cutting belt (193. On both sides of the belt (19), several people stand. These people cut up the animal (4a) into partial pieces either directly on the cutting ~elt (19) or at adjacent cutting workplaces (18).
Original Document 21~3~4 Partial pieces (4c) that do not need to be cut up~ further are packed in crates at forwarding or delivery stations (20) or attached to so called partial piece "pine trees."
In order to recognize and identify the slaughtered anim~ls (4b) and their partial pieces (4c), there are multipIe electronic cameras (8) located above the cutting zone and directed at the cutting belt (19), the cutting workplaces (18) and the forwarding or delivery stations (20).
Taken together, the cameras (8), with their adjustable picture areas, encompacs the entire cutting zone. The cutt;ng ~one is divided into individual control fields directly adjacent to or overlapping one another; a camera is assigned to each control feld of the picture area. All cameras (8) are conn~ed to a central computer (5). At all of the forwarding or del;very stations (20), there are label printers (17) controllable by the central computer (5), which print an identification and origin label ~r each partial piece ~4c) destined for forwarding or delivery.
Preferably, these labels are collagen labels, which have no deleterious effect on the meat in the area of adhesion.
On an ongoing basis, the cameras (8) above the cutting zone take digital pictures of the control fields with the slaughtered ~nim~l.c (4b) and/or partial pieces (4c) located therein. These pictures are stored in an electronic image memory of the central computer (5~ in pixel matrices. Each slaughtered animal (4b) and each partial piece (4c) has traits that are unambiguously characteristic for its identification, which can be determined using an image processing program and are summed up in a characteristic code.
Original I)ocument 21~5354 A neural network is simulated in the analysis computer. In the cells of the neural ne~work, expected values can be defined for each slaughtered animal (4b) and each partial piece created in the cutting process. The traits of a partial piece (4c~ determined by the image processing p~G~alll are then compared to the expected values for the purpose of identification. A
suitable identification address is then ~csi~necl to each partial piece (4c).
In the case of each successive partial piece (4c) created in the cutting process, several expected values are defined for external contours and the are~ enclosed therein from different points of view. In addition, expected values for the color composition of the partial piece surfaces are provided. The cutting order is also taken into account in the expected values. To identify and check a partial piece (4c~, a correlation is carried out between the traits determined using the image processing prograrn from the pictures taken and the expected values of the neural network.
The identification and control system according to the invention closes the gaps in the cutting zone in the l'chain of proof" that reaches from the agricultural producer operation to the supermarket. There is no interference with normal cutting operations.
In another example, the use of the process according to the invention is explained below in reference to sorting returned items at a book wholesaler. This appl;cation is not shown in the drawmgs.
Wholesalers often receive up to several thousand books per day that have been sent back by book dealers because of erroneous orders or damages Original Document 3~
or the like. Before being stored again, the books must first be sorted and brought to the correct warehouse locations.
For this purpose, the books are first placed upon a moving belt. A camera is mounted above the moving belt and takes a picture of each book. The picture is then ~ligit~ e(~ and ànalyzed. To this end, the characteristic traits of all possible books are stored in a central data base, as in a library.
In order to save time, the books in the present case are placed on the transport belt in a certain position. However, this is by no means absolutely necessary, because reorientation is also possible during analysis.
First, the height and width of the pictured book are determined on the basis of the picture information. This makes it possible to considerably reduce the search steps needed in the library to identify the book. Given a total of 6Q,000 possible books, for example, the number of data sets to be searched per book can be reduced to below 2000.
The picture section that contains the book is then transformed to a predetermined size. The image of the object is stored in a pixel matrix in the form of a gray scale value display.
Original ~ocl-m~ont
Processing, distribution and storage facilities usually have many dif~erent processing stations and transport paths. Depending on the tasks to be carried out and the final products, different objects must pass through different stations and the transport paths between them. As this happens, it is vital for the course of processing and transport steps to bç followed.
A typical example of an operation in which this problem is particularly important is a modern slaughterhouse. There are many different processing stations, e.g., entry scales, flaying stations, workplaces for removal of innards, washing and cleaning stations, meat inspection stations, stations for cutting the animalc into large and fine cuts, etc. In large part, the transport means on which the delivered slaughtered ~nirn~l~ pass through , ., . ~ ~, . - - - .
Original Document ~8~354 these stations and ehe transport paths between them are meat hooks. In the cutting zones, transport is frequently carried out on moving belts. It is necessa~y here to ensure not only that the delivered slaughtered animal, e.g., an ox or a hog, undergoes all of the correct processing steps, but also that the products obtained from a particular animal are unambiguously matched to that animal at all times.
A typical example of a distribution plant where correct transport paths within the plant are of crucial importance is a ~ook and periodical wholesaler. Each day, thousands of incoming returned items must be sorled and delivered to the appropriate warehouses. Markings that were originally found on the books or periodicals, such as bar codes and~ the like, have often been removed in the meantime or are no longer legible because o~ damage to the article.
Tke automatic monitoring and control of such a series of processing and transport operations is ~requently carried out by determining the respective locations within the plant of the different transport means.
One way to monitor the transport means is to equip these means with transmitters. A
method of this type is described in the German Document Laid Open to Public Inspection 37 11237 A1. In this instance, each transport container is equipped with a transponder. The transponder, which can be coded by a coding unit as often as desired, is electromagnetically excited in the vicinity of a transmitting antenna controlled by an inquiry system and thus emits a particular code, which in turn is received by the inquiry system.
Original Document -, 2185~5g Another common method is tO apply m~rkings or identific~tion means, e.g., bar codes, labels ~ith plain text symbols, etc., to the transport means or the transported or processed goods th~rnselves. These markings or id~rltifiers are read by suitable reading devices at the different stations.
~or exarnple, OS-l~E 32 05 189 A1 relates to a system for recognizing an orientation symbol, which serves as an aid in automatic code reading during the sorting of packages.
In all of these methods, the transport means or the transported or processed goods themselves must be specially equipped with identification means. When these ide~tification means are active means, such as transrnitters or transponders, they are subject to wear and tear. In the case of passive identification means, such as applied labels, the identification means frequently become impossible to read clearly after a certain period of time, because of dirt or partial damage; under some circumstances, they may even be read incorrectly.
The obje~ of the invention is to create a process for monitoring and/or controlling a predetermined series of transport and/or processing operations of transported and/or processed goods which does not require identification means or markings to be specially applied to the transport means or the processed or transported goods and which operates reliably and safely and can be easily installed.
This obiect is attained according to the invention in that at least a section of the object to be identified is photographed by an electronic camera. The Origin~l Document ~853'~
cha, a~ ;stic traits of the original surface and/or form of the o~ject are unambiguously derived from the digitalized photograph by an image pr~cessing prograrn and then are summed up in a characteristic code and stored and/or compared to previously stored characteristic codes for the purpose of iclentifi~tion.
The subci~im~ that follow the first claim contain advantageous embodiments and further developments of the invention.
The process on which the invention is based allows a wide variety of transported or processed goo~ or transport means, e.g., transport hooks, crates that move on conveyor belts, bottles, etc., to be identified.
Because the process accord;ng to the invention does not require markings of any sort to be applied to the objects to be identified, it is especially advantageous when there is an open circuit of objects, for example, when a plant uses no discrete tr~nsport means, such as crates, hooks or the like, that could be assigned to particular goods, or when external transport means enter the plant and subsequently leavç it again.
The characteristic codes of objects that enter the processing and transport sequence can be determined anew and stored by the system in each case, and then deleted when these objects leave the processing and transport sequence.
When changes occur in an object during processing, ~or example, when an object is divided up into multiple pieces or multiple pieces are combined to form one new piece, ()riginal Document - ~18~-~5l these changes can be determined and stored in the same ~ay.
It is also possible to sort types of objects on the basis of their characteristic traits.
Advanta~eously, the picture taken by the electronic camera is first stored in an electronic image memory of a computer in a pixel matrix.
All types of image processing routines and operations can be used on the picture stored in the pixel matrix. For example, a Laplace operation results in harder edges and greater image definition; mean value operations leads to smoothing at the expense of i~nage definition; and medi~ operations lead to smoothing while edges are maintained. Zoom operations allow particular details to be enlarged.
The pictllre of each object has characteristic traits that serve tO unarnbiguously identify the object. These traits can be determined by an image processing program and summed up in a characteristic code. Such traits include, for example, the shape and external dimensions of the object, edges, depressions, elevations, holes, scratches, labels, etc. On the basis of these traits, the unambiguous identification and recognition of objects, similar to the identification of persons on the basis of fingerprints, is possible. The position and size of the traits relatiYe to one another can be determined in the grid of the pixel matrix.
To allow different objects to be compared, the image processing program ad.~antageously standardizes ~he position of each particular picture by resorting or readdressing the pixels in the pixel Original Doc~lment ~18~35~
matrix according to predeterm;ned criteria. It is also advantageous to select an area in the position-standardized picture to be standardized by height and width, or to transform a section of the picture that includes the object to be identified or ~ predetermined part thereof to a predetermined size.
Preferably, a gray scale analysis is carried out with the standardized picture area. ~irst, the gray scale values of the individual pixels are determined and stored in a gray scale value matrix.
In addition, a gray scale histogram (c~. Fig. ~) can be created.
The gray scale values of individual pixels can be standardized relative to a pre-established value.
In this way, different light conditions can be taken into account.
The characteristic code of each particular object consists of different trait sets. Which trait sets are found and used for identification depends on the particular type of object, on the particular task (sorting known types of objects, identifying objects changed in the course of operations, etc.), on time-related information, e.g., identification speed, and on the degree of difficulty of the objects.
~;or example, the positional coordinates and dimensions of the primary picture components, i.e., object traits such as shape and size, edges, depressions, etc., can be included in a trait set (A1, A2, ...). Furthermore, it is possible to determine gray scale value distribution parameters, such as arithmetic mean v~lue, quadratic mean value, variance, half-value width, curtosis, etc., from the gray scaJe value histogram and to include these in another trait set (B1, B2, ... ).
Original Oocument - ~8 j354 In addition, a Fourier analys;s can be carried OUt with the gray scale value distribution, and the Fourier coefficients obtained from this can be included in another trait set ~C1, C2, ... ).
In a further advalltag~ous method, the picture is first transformed into a g~adient display, i which each pixel is replaced by the gradient occurring at that point. In this display, the characteristic traits of the pictured object with the strongest gradients are then determined, up to a predetermined number of traits. After this, the gradient display is converted into a display that discrimin~tes in reference tO a threshold value and stored The threshold value lies below the lowest gradient value of the previously deterrnined traits The result is a "black-white" image that constitutes the trait set in a matrix representation, wherein only the most strongly characterizing traits are present In this way, the memory required for an image can be reduced significantly. The image obtained contains only the important information, i.e, the "fingerprint" of the object in question So that it is possible for an object tO be recognized despite any slight changes that might have occurred in its "fingerprint," it is advantageous to blur the black-white images that are to be compared, i e, to make them less sharp This is ideally done by replacing, prior to producing the gradient display, the gray scale value of each pixel by the median of the gray scale values of the pixel itself and the pixels located in a given environment surrounding of the pixel.
Another option is to process the image, prior to producing the gradient display, with a blurring filter that replaces the discrete gray scale values at the individual pixels by a Gaussian distribution at a given width and Original Document ~8~354 `
from this generates a new, less sharp gray scale value image. This process step can also be carried out with the gradient display prior to discrimination relative to a threshold va~ue.
Another simple method-which, however, produces no defined blurring-is to merely set the lens somewhat out o~ focus while the pictures are being taken by the camera.
Different minimum agreement conditions (WA, WB, WC, ...), which must be ~.ceede~l during a comparison for the purpose of identification, can be established for different types of trait sets.
Advantageously, prior to entering the processing or transport sequence, the objects are first photographed by an electronic camera at an entry station; the data are stored in a central data base. All other computers at the individual observation stations, as well as a central computer, are networked with this central data base.
A processing or transport plan, which indicates what object is to pass through what station at what time, can be stored in the central computer, for exarnple.
When types of obiects are to be sorted on the basis of their (known) characteristic traits, it is also possible to place a library of object traits or even entire object pictures in the central data base of the central computer.
If new objects are created during the course of processing, the characteristic codes of these objects or the expected values ~or such characteristic codes can be s~ored in the central data base or in Original I~ocument - 218~354 the cells of a neural networl~ simulated by the central computer. The expected values for the~e new objects can include, for example, the external contours and the area encolllpassed therein from various points of view. This takes into account the fact that objects pass by the control station at di~l~ent orientations. Along ~ith data on the expected contours of a particular ob~ect, expected values related to color composition or brightness variations on the surface can also be provided.
It is also advantageous for the characteristic code of each object to include the time of registration at the entry station. Based on the stored operational plan and the times at which particular objects entered the processing or transport sequence, it is then 'possible, taking into account the transport and processing times, to calculate in advance which objects can be expected at a given control station and given time intervals with a particular probab;lity.
Then, in order to identify an obje~ at the gi~en control station, only the characteristic codes of these particular groups of objects are used initially for agreement comparison. In this way, recognition time can be significantly reduced.
The control stations signal to the central computer which of the objects has reached or passed through a certain processing station or transport path. These data can be checked in the central computer on the basis of the stored operational plan; in addition, the correct routes c~n be set for conveying the object from this point to the next processing station or transport path.
Onginal I~ocument ~1~5354 Prior to leaving the transport or processing area, tbe objects are advantageously photographed again by an electronic camera at an exit station. The camera signals the exiting objects to the central computer for control purposes. A job completion message can then be issued and the characteristic code of the object removed from the central data base. If needed, a device that provides each outgoing object with proof of identification and origin can be activated. For example, this can ~e a label printer activated by the central computer that prints an identification and origin label for each object destined for forwarding or delivery, which is then stuck onto the object.
For optimal lltili7~tion of computer capacities, it is advantageous ~or object-sign~lling in;tiators, such as light barriers or mech~nical or inductivç contact switches, to be arranged at the individual stations. When an object is located in the precise image area of the camera in question, these initiators send a release signal to the carnera, so that only in these instances are digital pictures taken and stored. In th;s way, the unnecessary analysis of pictures that contain no objects is avoided.
When a series of operations, for example, on an assembly line, must be controlled, complete control areas can be formed by combining the directly adjacent or overlapping image areas of different cameras. These areas can in turn be divided into directly adjacent or overlapping control fields. If desired, one control field can correspond to the image area of one carnera;
however, this is not absolutely necessary. As a result, it is also possible to follow the objects and their processing in a larger area.
Original Document ~18~35~
Of course, it is also possible, if desired, to divide an image area that can be seen by ~ single camera into several control fields during analysis.
Original Doc-~m~nt ~185~-5g The invention is described below in greater detail in reference to Figures 1 to 8 using the example of use in a slaughterhouse. The drawings show:
Fig. 1 Schçm~tic overview of a slaughterhouse;
Fig. lA A control station with a meat hook, in side view, and a camera directed at this with a light barrier as the release initiator for the camera;
Fig. 2 A rear view of a meat hook as seen from the camera;
Fig. 3 A pixel matrix with a sectional image of a meat hook; resolution into individual pixels is not shown;
Fig. 4 A pixel matrix with a standardized image area of the mea~ hook;
Fig. 5 A gray value scale histogram of the standardized image area;
Fig. 6 A side view of a transport path, with a suspended slaughtered animal, and a cutting belt with cameras arranged above it;
Fig. 7 A top view of the cutting belt with slaughtered anim~ls and animal parts distributed thereon;
Fig. 8 A side view of the cutting belt with a camera arranged above it and the adjacent cutti~g workplace.
Original Document 8~3~4 Figure 1 shows a schematic overview of a slaughterhouse. In the s3aughterhouse, there are a large number of different processing stations (1) and transport paths (2). The anim~ls delivered for slaughter are killed at a first station (3), suspended on a meat hook (4a) that serves as the transport means, transported to an entry scale, and then transported further to the individual processing stations (1), e.g., flaying stations, workplaces for removal of innards, washing and cleaning stations, meat inspection points, large and fine cutting stations, etc.
Prior to entering the processing sequence, e.g., a~ter the killing station (3), each meat hook (4a) with a slaughtered anim~l suspended on it is registered and assigned a job number or meat hook number A processing plan stored in a central computer registers the correct entry time and establishes which processing stations must be passed through by the meat hook with the slaughtered ~nimal suspended thereon and through which exit station the meat hook is to leave the slaughterhouse.
The following diagr~m shows an excerpt from such a processing plan:
Job No./ Entry Processing Stations Exit Stations Meat Time Hook No. V1 V2 V3 ... A1 A2 A3 ...
9:53 1 2 3 2 4 8 2 9:56 3 6 4 5 7 24 3 9:59 20 5 8 52 27 30 4 10:03 14 9 19 54 34 37 ... ... ... ... ... ... ... ...
71 11:20 34 40 20 60 37 40 72 11:27 41 47 23 67 38 45 . . .
Replacement Page (Rule 26) 2~8~54 A meat hook (4a) that has entered the sl~ughterhouse is registered at an entry station (7). At the entry station, a digital sectional picture (9~ is taken (cf. Figs. lA and 3) by a first electronic camera (8) and stored in a pixel mat~ix (11) in an electronic image memory of a computer (10).
Figure 4 shows a pixel matrix (11) con~isting of 512 x 512 pixels.
However, the photographic matrix ~11) may also include more or fewer pixels, e.g., 256 x 256 or 1024 x 1024. The sectional picture (9) of each meat hook ~4a) has ~1n~nbiguous characteristic traits identi~ying the meat hook (4a), which can be determined by means of an image processing program and summed up in a characteristic code. The characteristic code of each meat hook (4a) is stored in a central data base (5A). Characteristic traits in~lude, for example, edges, depressions, elevations, scratches, holes and the like. An unambiguous identification is possi~le on the ~asis of these characteristics.
In order to identify and recognize the particular meat hook (4a), its edges, depressions, elevations and/or holes are found in the sectional picture (9), and their position and dimensions in the grid of the pixel matrix (11) and relative to one another are determined by means of an image processlng program.
.
Positional standardization of the particular sectional picture (9) is carried out by resorting or readdressing the pixels in the grid of the pixel matrix (11) on t~le basis of predetermined criteria using an image processing prograrn. A piclure area (~A) standardized by height and width is then selected in the position-standardized sectional picture (9).
Original ~ocument 5 ~
Using the standardized pictllre area (9A), a gray scale value analysis is carried out. The gray scale values of the individual pixels are found and stored in a gray scale value matrix.
An image of this type enconlpa~es 4096 bytes. 'rhe gray scale value of eæh pixel is then replaced by the median of the gray scale values of the pixel and the pixels located in a predetermined environment surrounding the pixel. After this, the picture is transformed into a gradient image, i.e., the individual pixel value is replaced by a value corresponding to the gradient at this location. In this gradient display, the traits most strongly characteristic of the pictured object, i.e., the traits with the strongest gradients, are found, up to a predetermined number of traits. A number of c. 800 traits has proved sufficient here. ~Next, the p;cture in the gradient display is prc~essed with a (mathematic~l) blurring filter, blurring the contours of the characteristic traits. Finally, the gradient display is converted into a display that discrimin~tes in reference to a threshold value and then stored. The threshold value is lower than the lowest gradient value of the previously determined traits.
The result is ~ black-white image in which only the characterizing traits appear. The picture still has a memory requirement of 512 bytes. This "fingerprint" of the object is then stored as the trait set.
Blurring contours by ~orming the median or applying the blurring filter is done so that even if the "fingerprin~s" have changed slightly, the passing objects can still be classified.
Original l:)ocument ~85~5g Irlstead of the rather complex mathematical contour blurring, the camera lens can be set somewhat out of focus before the picture is taken. The process takes on some ~e~el of uncertainty as a result, on the other hand, however, considerable time is gained. After this, as described above in reference to the exarnple of meat hook recognition, a "black-white fingerprint" of the picture is produced as the essential trait set of the object. This picture ;s then compared bit-by-bit to stored pictures that are possible candidates for agreement, and the picture with the greatest ~greement is sought. The time needed to identify a book by this method can be less than 100 ms.
After the book has been identified, the computer issues appropriate ins~ructions, so that the book makes it way to the desired warehouse area. At the same time, the pertinentinformation is passed along to the bookkeeping department of the warehouse, etc, Before and/or after each of the processing stations (I) or transport paths (2), a digital sectional picture (9) of a particular meat hook (4a) is taken by a further electronic camera (8) at the control stations (12) and stored in a pi~zel matrix (11) in a digital image memory of a computer (10). To recognize the particular meat hook (4a), the sectional picture (9) is analyzed by an image processing program, as described above, and compared to the characteristic codes stored in the central data base (SA).
Identification is carried out essentially by means of the bit-by-bit comparison of the "black-~hite fingerprints" stored as the matrix-form trait sets. The Orig~nal Document - 21~5~
picture of the hook (4a) to be identified is compared to all other pictures of hooks ~4a) in circulation that are possible candidates, and the hook (4a) with the greatest agreçment ~s sought. A certain minimum agreement must be achieved, however. The minimurn agreement threshold can be selected depending on the requirements of the particular plant. An advantageous value is found at agreement of 60%.
Other trait sets ~e.g., time of previous or entry-side registration) are used for comparison, as needed, in order to limit the number of "black-white images" to be compared. For example, based on the processing plan and the entry times of the meat hooks (4a) into the prc~cessing sequence, and taking into account the transport and processing times, it is possible to calculate in advance which meat hooks (4a) will, with a particular probability, be at certain control or exit stations (6, 12) at particular time intervals. In order to identify a meat hook (4a) at a particular control or exit station (6, 12), only the characteristic code of this group of transport means (4) is initially used for agreement compa~ison. In this way, recognition time is significantly reduced.
After this, the centra~ computer (5) is signalled which of the meat hooks (4a) has reached or passed through the particular processing station (1) or transport path (2). On the basis of the processing pian stored in the central computer (5), a check is made to determine whether it is correct for the identified meat hook (4a) to be at the control station in question. As needed, a router (13) is set for conveying the meat hoo~ (4a) to the next processing station (1) or transport path (2).
Original I)ocument 218~54 Exit stations (6) are located at the slaughterhouse delivery ramps (14), at each of which another electronic camera (8) takes a digital sectional picture (9) of the respective meat hooks (4a). The picture is stored in an electronic image memory of a computer (10). In order to recognize the meat hook (4a), the sectional picture ~9) is analyzed by an image p,ocess;ng program and compared to the characteristic codes stored in the central data base (SA). The central computer (5) is signalled which of the meat hooks ~4a) has reached the exit station (6) in question. A check is made, in reference to the proçessing plan, to determine whether it is correct for the identified meat hook (4a) to be at the exit station (6) in question. As needed, a job connpletion message or the like is issued and the characteristic code of this meat hook (4a) is removed from the central data base (5A).
At the respective entry stations (7), control stations (12) and exit stations (6), there is an initiator, preferably a light barrier (1~, 15'), which signalizes the meat hooks (4a) and emits a release signal (AS) for the appropriate camera (8), so that a digital sectional picture (9) of the sign~li7ed meat hook (4a) is taken by the camera (8) and stored. In addition, the time of registration is signalled to the central computer (5) and can be included in the job protocol.
Instead of a light barrier (15, 15'), the initiator may also be a mechanical or inductive contact switch.
In an alternative embodiment, pictures are taken by the particular camera (3) at periodic sequential time intervals that are substantially shorter than the passage times of the meat hooks (4a) through the Ori~inal Document -control stations (12). In each case, a rough linear or columnar grid is established in order to determine whether a meat hook (4a) is contained in the picture or not. If there is a meat hook (4a) in the picture, the picture is stored for further analysis.
The central computer (~), the central data base (~A) and all of the other computers (10) are networked ~vith one another.
The picture signals Q3S) ~rom the entry, control and exit stations are sent to the central computer (5~. The routing instruction signals (WS) are established by the central computer (5).
Until re~ching the cutting zone (16), the individual slaughtered anim~ls (4b) are transported suspended on meat hooks (4a) and via transport rails (2). The process according to the invention makes it possible to know which slaughtered ~nimal~ (4b) are suspended on which meat hooks (4a).
When the cutting zone (16) is reached, the exit station (6) for meat-hook monitoring turns over the appropriate identification start address to the analysis computer (5); advantageously, the arrival or start time is stored at the same time.
The cutting zone (16) consists of a cutting belt (19) and multiple cutting workplaces (lB). The slaughtered animals (4b) are moved from the transport path (2) onto the cutting belt (193. On both sides of the belt (19), several people stand. These people cut up the animal (4a) into partial pieces either directly on the cutting ~elt (19) or at adjacent cutting workplaces (18).
Original Document 21~3~4 Partial pieces (4c) that do not need to be cut up~ further are packed in crates at forwarding or delivery stations (20) or attached to so called partial piece "pine trees."
In order to recognize and identify the slaughtered anim~ls (4b) and their partial pieces (4c), there are multipIe electronic cameras (8) located above the cutting zone and directed at the cutting belt (19), the cutting workplaces (18) and the forwarding or delivery stations (20).
Taken together, the cameras (8), with their adjustable picture areas, encompacs the entire cutting zone. The cutt;ng ~one is divided into individual control fields directly adjacent to or overlapping one another; a camera is assigned to each control feld of the picture area. All cameras (8) are conn~ed to a central computer (5). At all of the forwarding or del;very stations (20), there are label printers (17) controllable by the central computer (5), which print an identification and origin label ~r each partial piece ~4c) destined for forwarding or delivery.
Preferably, these labels are collagen labels, which have no deleterious effect on the meat in the area of adhesion.
On an ongoing basis, the cameras (8) above the cutting zone take digital pictures of the control fields with the slaughtered ~nim~l.c (4b) and/or partial pieces (4c) located therein. These pictures are stored in an electronic image memory of the central computer (5~ in pixel matrices. Each slaughtered animal (4b) and each partial piece (4c) has traits that are unambiguously characteristic for its identification, which can be determined using an image processing program and are summed up in a characteristic code.
Original I)ocument 21~5354 A neural network is simulated in the analysis computer. In the cells of the neural ne~work, expected values can be defined for each slaughtered animal (4b) and each partial piece created in the cutting process. The traits of a partial piece (4c~ determined by the image processing p~G~alll are then compared to the expected values for the purpose of identification. A
suitable identification address is then ~csi~necl to each partial piece (4c).
In the case of each successive partial piece (4c) created in the cutting process, several expected values are defined for external contours and the are~ enclosed therein from different points of view. In addition, expected values for the color composition of the partial piece surfaces are provided. The cutting order is also taken into account in the expected values. To identify and check a partial piece (4c~, a correlation is carried out between the traits determined using the image processing prograrn from the pictures taken and the expected values of the neural network.
The identification and control system according to the invention closes the gaps in the cutting zone in the l'chain of proof" that reaches from the agricultural producer operation to the supermarket. There is no interference with normal cutting operations.
In another example, the use of the process according to the invention is explained below in reference to sorting returned items at a book wholesaler. This appl;cation is not shown in the drawmgs.
Wholesalers often receive up to several thousand books per day that have been sent back by book dealers because of erroneous orders or damages Original Document 3~
or the like. Before being stored again, the books must first be sorted and brought to the correct warehouse locations.
For this purpose, the books are first placed upon a moving belt. A camera is mounted above the moving belt and takes a picture of each book. The picture is then ~ligit~ e(~ and ànalyzed. To this end, the characteristic traits of all possible books are stored in a central data base, as in a library.
In order to save time, the books in the present case are placed on the transport belt in a certain position. However, this is by no means absolutely necessary, because reorientation is also possible during analysis.
First, the height and width of the pictured book are determined on the basis of the picture information. This makes it possible to considerably reduce the search steps needed in the library to identify the book. Given a total of 6Q,000 possible books, for example, the number of data sets to be searched per book can be reduced to below 2000.
The picture section that contains the book is then transformed to a predetermined size. The image of the object is stored in a pixel matrix in the form of a gray scale value display.
Original ~ocl-m~ont
Claims (24)
1. Process for monitoring and/or controlling a predetermined series of transport and/or processing operations by recognizing and registering the transported and/or processed goods or the transport means (object) at different stations, characterized by the fact that a picture of at least one section (7) of the object (4) is taken by an electronic camera (8) and the traits of the original surface and/or form of the object that unambiguously characterize the object (4) are extracted in an image processing program from the picture (9) that has been taken and digitalized and are summarized in a characteristic code and stored and/or are compared to already stored characteristic codes for the purpose of identification.
2. Process as in Claim 1, characterized by the fact that the picture (9) taken by the electronic camera (8) is stored in an electronic image memory of a computer (10) in a pixel matrix (11).
3. Process as in Claim 2, characterized by the fact that in the picture (9) of the particular object (4), for the purpose of identifying the same, the form and size of the object, edges, depressions, elevations, holes and/or brightness and/or color variations and their position and dimensions in the grid of the pixel matrix (11) and relative to one another are determined using the image processing program,.
4. Process as in one of the above claims, characterized by the fact that by resorting or readdressing the pixels in the grid of the pixel matrix (11) according to predeterminable criteria, a positional standardization of each particular picture (9) is carried out using the image processing program.
5. Process as in one of the above claims, characterized by the fact that a picture area (9A) standardized according to height and width is selected in the position-standardized picture (9).
6. Process as in one of the above claims, characterized by the fact that a section of the picture (9) that shows the object (4) to be identified or a predetermined portion thereof is transformed to a given size.
7. Process as in one of the above claims, characterized by the fact that a gray scale value analysis is undertaken with the standardized picture area (9A), whereby the gray scale values of the individual pixels are determined and stored in a gray scale value matrix and/or a gray scale value histogram is created.
8. Process as in one of the above claims, characterized by the fact that the gray scale values of the individual pixels are standardized in reference to a predeterminable value.
9. Process as in one of the above claims, characterized by the fact that the position coordinates and the dimensions of picture components are collected and stored in a trait set (A1, A2, ...).
10. Process as in one of the above claims, characterized by the fact that for the gray scale value histogram, the arithmetic mean value and/or the quadratic mean value and/or the variance and/or the half-value width and/or the curtosis are determined and summed up in a trait set (B1, B2, ...) and stored.
11. Process as in one of the above claims, characterized by the fact that a Fourier analysis is carried out with the gray scale value distribution and the appropriate Fourier coefficients are summed up in a trait set (C1, C2, ... ) and stored.
12. Process as in one of the above claims, characterized by the fact that the picture (9) is transformed into a gradient display, and that in this display the traits characterizing the pictured object with the strongest gradients are determined, up to a given number, and that then the gradient display is converted into a display that discriminates in reference to a threshold value and is stored as a trait set, whereby the threshold value lies under the lowest gradient value of the previously determined traits.
13. Process as in Claim 12, characterized by the fact that prior to generation of the gradient display, the gray scale value of each pixel is replaced by the median of the gray scale values of the pixel and the pixels located in a given environment surrounding the pixel and/or prior to generation of the gradient display, the picture and/or after the determination of the traits with the strongest gradients and prior to the discrimination in reference to a threshold value, the gradient display is blurred by means of a blurring filter.
14. Process as in one of the above claims, characterized by the fact that for the purpose of unambiguous characterization the different trait sets (A1, A2, ...), (B1, B2, ...), (C1, C2, ...), ..., (N1, N2, ... ) are summed up in the characteristic code and stored.
15. Process as in one of the above claims, characterized by the fact that in order to compare the individual types of trait sets, respective different minimum agreement conditions (WA, WB, WC, ...) can be established.
16. Process as in one of the above claims, characterized by the fact that the trait sets of the object (4) determined in the case of a picture being taken of the object (4) at an entry station (7) located at the beginning of the transport and/or processing sequence are stored in a central data base (5A) and that at control stations (12) located before and/or after the particular processing stations (1) or transport paths (2) or at an exit station (6) these trait sets are determined anew from the object (4) to be identified and for the purpose of identification are compared to the trait sets of the object (4) in circulation that are stored in the central data base (5A) and summed up in the characteristic code.
17. Process as in one of the above claims, characterized by the fact that a central computer (5) is connected to the central data base (5A) and that all other computers (10) are networked with this central computer (5) and the data base (5A) and/or one another.
18. Process as in one of the above claims, characterized by the fact that for the objects (4) passing through the transport and/or processing plant and/or created during the processing operation, characteristic codes are stored in the central data base (5A) and/or in the cells of a neural network simulated by the central computer (6).
19. Process as in one of the above claims, characterized by the fact that the time of registration at the entry station (7) is recorded in the characteristic code of each object (4).
20. Process as in one of the above claims, characterized by the fact that on the basis of the processing plan and the particular entry times of objects (4) into the processing sequence, and taking into account the transport and processing times, it is calculated in advance in the central computer (5) which objects (4) will be at a particular control (12) or exit station (6) at particular time intervals with a given probability, whereby for the purpose of identifying an object (4) at the particular control (12) or exit station (6) initially only the characteristic codes of this group of objects (4) are used for an agreement comparison.
21. Process as in one of the above claims, characterized by the fact that by means of the control stations (12) it is signalled to the central computer (5) which of the objects (4) has reached or passed through a certain processing station (1) or transport path (2), and on the basis of a processing plan stored in the central computer (5) a check is made to determine whether it is correct for the identified object (4) to be at this control station (12), and on the basis of the processing plan a router (13) is set for conveying the object (4) to the next processing station (1) or transport path (2), as necessary, and that at the exit stations (6) it is signalled to the central computer (5) which of the objects (4) has reached the particular exit station (6), and on the basis of the processing plan a check is made to determine whether it is correct for the identified object (4) to be at this exit station (6) and/or a job completion message is sent to the central computer (5) and/or the characteristic code of this object (4) is removed from the central data base (5A) and/or a device (17) is activated which provides every outgoing object with a proof of identification and origin label.
22. Process as in one of the above claims, characterized by the fact that at the entry stations (7), the control stations (12) and the exit stations (6), there are initiators that signalize the object (4), such as light barriers (15, 15') or mechanical or inductive contact switches, and when the initiator in question gives a release signal, a digital picture (9) of the particular object (4) is taken by the corresponding camera (8) and stored.
23. Process as in one of the above claims, characterized by the fact that the objects (4) are meat hooks (4a) with which slaughtered animals are transported past different processing stations (6, 12) of a slaughterhouse and/or are partial pieces (4c) of slaughtered animals (4b) in a cutting zone (16) of a slaughterhouse.
24. Process as in one of the above claims, characterized by the fact that the objects (4) are books that are sorted according to size and/or title image and/or backs.
Applications Claiming Priority (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE4408650A DE4408650C2 (en) | 1994-03-15 | 1994-03-15 | Identification and control system for means of transport in a processing plant |
| DEP4408650.4 | 1994-03-15 | ||
| DE19502689.6 | 1995-01-28 | ||
| DE1995102689 DE19502689C2 (en) | 1995-01-28 | 1995-01-28 | Identification and control system for carcass cutting in a cutting plant and / or slaughterhouse |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2185354A1 true CA2185354A1 (en) | 1995-09-21 |
Family
ID=25934703
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA002185354A Abandoned CA2185354A1 (en) | 1994-03-15 | 1995-03-13 | Identification and control system for processed and/or transported goods |
Country Status (8)
| Country | Link |
|---|---|
| EP (1) | EP0752137B1 (en) |
| AT (1) | ATE186791T1 (en) |
| AU (1) | AU1950595A (en) |
| CA (1) | CA2185354A1 (en) |
| DE (2) | DE4408650C2 (en) |
| DK (1) | DK0752137T3 (en) |
| ES (1) | ES2141343T3 (en) |
| WO (1) | WO1995025315A1 (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017174768A1 (en) * | 2016-04-08 | 2017-10-12 | Teknologisk Institut | System for registration and presentation of performance data to an operator |
| WO2019078944A1 (en) * | 2017-10-20 | 2019-04-25 | BXB Digital Pty Limited | Systems and methods for tracking goods carriers |
| WO2019232113A1 (en) * | 2018-06-01 | 2019-12-05 | Cryovac, Llc | Image-data-based classification of meat products |
| US10977460B2 (en) | 2017-08-21 | 2021-04-13 | BXB Digital Pty Limited | Systems and methods for pallet tracking using hub and spoke architecture |
| US11062256B2 (en) | 2019-02-25 | 2021-07-13 | BXB Digital Pty Limited | Smart physical closure in supply chain |
| US11244378B2 (en) | 2017-04-07 | 2022-02-08 | BXB Digital Pty Limited | Systems and methods for tracking promotions |
| US11249169B2 (en) | 2018-12-27 | 2022-02-15 | Chep Technology Pty Limited | Site matching for asset tracking |
| US11507771B2 (en) | 2017-05-02 | 2022-11-22 | BXB Digital Pty Limited | Systems and methods for pallet identification |
| US11663549B2 (en) | 2017-05-02 | 2023-05-30 | BXB Digital Pty Limited | Systems and methods for facility matching and localization |
| US11900307B2 (en) | 2017-05-05 | 2024-02-13 | BXB Digital Pty Limited | Placement of tracking devices on pallets |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE19824323B4 (en) * | 1998-06-02 | 2004-01-29 | Mvs-Imci Maschinen- Und Verpackungs-Service Gmbh | Method for monitoring the passage of a group of independent objects through a passage area and monitoring system |
| DE10155780A1 (en) * | 2001-11-14 | 2003-05-22 | Vision Tools Hard Und Software | Securing objects against counterfeiting and/or imitation involves inserting and/or attaching non-reproducible physical random pattern during manufacture for later detection and comparison |
| DE10210949A1 (en) * | 2002-03-13 | 2003-10-09 | Geutebrueck Gmbh | Area-surveillance equipment includes both wide-angle and narrow angle cameras connected to display showing entire area with detailed, inset image |
| EP1716755A3 (en) * | 2002-03-18 | 2006-11-15 | Scanvaegt International A/S | Method and system for monitoring the processing of items |
| FR2841673B1 (en) † | 2002-06-26 | 2004-12-03 | Solystic | TIMING OF POSTAL OBJECTS BY IMAGE SIGNATURE AND ASSOCIATED SORTING MACHINE |
| DE102008025659A1 (en) * | 2008-05-28 | 2010-01-21 | Wincor Nixdorf International Gmbh | Method and device for avoiding aliasing in the optical detection of transport containers |
| WO2014072497A1 (en) * | 2012-11-12 | 2014-05-15 | Deutsche Post Ag | Franking for items of mail |
| KR101947444B1 (en) * | 2016-06-08 | 2019-02-13 | 주식회사 태진 | Quality Management Method of Butchered Chicken |
| DE102017110861A1 (en) | 2017-05-18 | 2018-11-22 | Ssi Schäfer Automation Gmbh | Apparatus and method for controlling a flow of material at a material flow node |
| CN113052835B (en) * | 2021-04-20 | 2024-02-27 | 江苏迅捷装具科技有限公司 | Medicine box detection method and system based on three-dimensional point cloud and image data fusion |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE3205189C2 (en) * | 1982-02-13 | 1985-09-12 | Brown, Boveri & Cie Ag, 6800 Mannheim | Arrangement for recognizing an orientation mark |
| JPH0615140B2 (en) * | 1982-10-29 | 1994-03-02 | 株式会社日立製作所 | Production processing method in production processing system |
| US4878176A (en) * | 1984-05-04 | 1989-10-31 | Asics Corporation | Production process control system |
| DE3711237C2 (en) * | 1987-04-03 | 1995-08-03 | Knapp Guenter Gmbh Co Kg | Method and device for controlling piece goods conveyor systems with the help of transponders |
| DE3942009C2 (en) * | 1989-12-20 | 1994-03-03 | Deutsche Aerospace | System for controlling and monitoring the distribution of goods |
| AU7251591A (en) * | 1990-01-29 | 1991-08-21 | Technistar Corporation | Automated assembly and packaging system |
-
1994
- 1994-03-15 DE DE4408650A patent/DE4408650C2/en not_active Expired - Fee Related
-
1995
- 1995-03-13 DE DE59507251T patent/DE59507251D1/en not_active Ceased
- 1995-03-13 ES ES95912247T patent/ES2141343T3/en not_active Expired - Lifetime
- 1995-03-13 CA CA002185354A patent/CA2185354A1/en not_active Abandoned
- 1995-03-13 AU AU19505/95A patent/AU1950595A/en not_active Abandoned
- 1995-03-13 EP EP95912247A patent/EP0752137B1/en not_active Expired - Lifetime
- 1995-03-13 DK DK95912247T patent/DK0752137T3/en active
- 1995-03-13 AT AT95912247T patent/ATE186791T1/en active
- 1995-03-13 WO PCT/EP1995/000922 patent/WO1995025315A1/en active IP Right Grant
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017174768A1 (en) * | 2016-04-08 | 2017-10-12 | Teknologisk Institut | System for registration and presentation of performance data to an operator |
| US11244378B2 (en) | 2017-04-07 | 2022-02-08 | BXB Digital Pty Limited | Systems and methods for tracking promotions |
| US11507771B2 (en) | 2017-05-02 | 2022-11-22 | BXB Digital Pty Limited | Systems and methods for pallet identification |
| US11663549B2 (en) | 2017-05-02 | 2023-05-30 | BXB Digital Pty Limited | Systems and methods for facility matching and localization |
| US11900307B2 (en) | 2017-05-05 | 2024-02-13 | BXB Digital Pty Limited | Placement of tracking devices on pallets |
| US10977460B2 (en) | 2017-08-21 | 2021-04-13 | BXB Digital Pty Limited | Systems and methods for pallet tracking using hub and spoke architecture |
| WO2019078944A1 (en) * | 2017-10-20 | 2019-04-25 | BXB Digital Pty Limited | Systems and methods for tracking goods carriers |
| US10956854B2 (en) | 2017-10-20 | 2021-03-23 | BXB Digital Pty Limited | Systems and methods for tracking goods carriers |
| AU2018353840B2 (en) * | 2017-10-20 | 2021-09-16 | BXB Digital Pty Limited | Systems and methods for tracking goods carriers |
| WO2019232113A1 (en) * | 2018-06-01 | 2019-12-05 | Cryovac, Llc | Image-data-based classification of meat products |
| US11249169B2 (en) | 2018-12-27 | 2022-02-15 | Chep Technology Pty Limited | Site matching for asset tracking |
| US11062256B2 (en) | 2019-02-25 | 2021-07-13 | BXB Digital Pty Limited | Smart physical closure in supply chain |
Also Published As
| Publication number | Publication date |
|---|---|
| WO1995025315A1 (en) | 1995-09-21 |
| ATE186791T1 (en) | 1999-12-15 |
| EP0752137B1 (en) | 1999-11-17 |
| EP0752137A1 (en) | 1997-01-08 |
| AU1950595A (en) | 1995-10-03 |
| DE4408650A1 (en) | 1995-09-21 |
| DE59507251D1 (en) | 1999-12-23 |
| DK0752137T3 (en) | 2000-05-08 |
| DE4408650C2 (en) | 1996-01-18 |
| ES2141343T3 (en) | 2000-03-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CA2185354A1 (en) | Identification and control system for processed and/or transported goods | |
| AU2007242918B2 (en) | Method and system for associating source information for a source unit with a product converted therefrom | |
| US6148249A (en) | Identification and tracking of articles | |
| KR102010494B1 (en) | Optoelectronic code reader and method for reading optical codes | |
| US20120070086A1 (en) | Information reading apparatus and storage medium | |
| US8295583B2 (en) | System and method for automatic recognition of undetected assets | |
| MX2007001504A (en) | Systems and methods for using radio frequency identification tags to communicating sorting information. | |
| US20020012419A1 (en) | Analysis of samples | |
| CN113761962B (en) | Visual detection method, system and storage medium for code-giving product | |
| US20210374664A1 (en) | Method and apparatus for tracking, damage detection and classification of a shipping object using 3d scanning | |
| CN115905733A (en) | Mask wearing abnormity detection and trajectory tracking method based on machine vision | |
| CN112069841B (en) | X-ray contraband parcel tracking method and device | |
| US20240000088A1 (en) | A method of tracking a food item in a processing facility, and a system for processing food items | |
| AU2021107671A4 (en) | Packaging compliance method | |
| EP4025058B1 (en) | Automatic removal of contamination on carcasses | |
| CN116029318A (en) | Automatic code scanning method, device, equipment and storage medium | |
| US10545095B1 (en) | Hide grading system and methods | |
| KR20240004292A (en) | How to Comply with Packaging Regulations | |
| CN117132294A (en) | Livestock product traceability evidence searching method, application program and system | |
| CN115565138A (en) | Smoking behavior control method, device, equipment and storage medium for logistics area | |
| RU2021115061A (en) | METHOD AND DEVICE FOR CONTROL OF LABELS ATTACHED TO FOOD PACKAGING | |
| CN114701264A (en) | Book collection and editing preprocessing method, device and system | |
| DK161725B (en) | Unit for processing carcasses on a slaughter line and gambrel provided with an automatically readable identification mark | |
| NZ556253A (en) | Object tracking method and apparatus | |
| Hilton et al. | Sheep pelt inspection |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| EEER | Examination request | ||
| FZDE | Discontinued | ||
| FZDE | Discontinued |
Effective date: 20000313 |