CA2147602A1 - Fish sorting machine - Google Patents
Fish sorting machineInfo
- Publication number
- CA2147602A1 CA2147602A1 CA002147602A CA2147602A CA2147602A1 CA 2147602 A1 CA2147602 A1 CA 2147602A1 CA 002147602 A CA002147602 A CA 002147602A CA 2147602 A CA2147602 A CA 2147602A CA 2147602 A1 CA2147602 A1 CA 2147602A1
- Authority
- CA
- Canada
- Prior art keywords
- fish
- values
- image
- colour
- species
- 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
- 241000251468 Actinopterygii Species 0.000 claims abstract description 205
- 238000004458 analytical method Methods 0.000 claims abstract description 22
- 238000003860 storage Methods 0.000 claims abstract description 6
- 235000019688 fish Nutrition 0.000 claims description 200
- 241000894007 species Species 0.000 claims description 44
- 238000000034 method Methods 0.000 claims description 21
- 230000006870 function Effects 0.000 claims description 11
- 238000000491 multivariate analysis Methods 0.000 claims description 8
- 238000012360 testing method Methods 0.000 claims description 7
- 241000269978 Pleuronectiformes Species 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000009826 distribution Methods 0.000 claims description 2
- 230000007246 mechanism Effects 0.000 claims 2
- 238000003909 pattern recognition Methods 0.000 description 6
- 238000005259 measurement Methods 0.000 description 5
- 241000276438 Gadus morhua Species 0.000 description 4
- 241001676635 Lepidorhombus whiffiagonis Species 0.000 description 4
- 241000276489 Merlangius merlangus Species 0.000 description 4
- 241001481828 Glyptocephalus cynoglossus Species 0.000 description 3
- 241001417045 Lophius litulon Species 0.000 description 3
- 241000269908 Platichthys flesus Species 0.000 description 3
- 241000269907 Pleuronectes platessa Species 0.000 description 3
- 241001290266 Sciaenops ocellatus Species 0.000 description 3
- 241001486863 Sprattus sprattus Species 0.000 description 3
- 241001233037 catfish Species 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 241001527902 Aratus Species 0.000 description 2
- 241000252203 Clupea harengus Species 0.000 description 2
- 241000276495 Melanogrammus aeglefinus Species 0.000 description 2
- 241001502127 Mullus barbatus Species 0.000 description 2
- 241000276498 Pollachius virens Species 0.000 description 2
- 210000001015 abdomen Anatomy 0.000 description 2
- 238000012505 colouration Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 235000019514 herring Nutrition 0.000 description 2
- 238000010191 image analysis Methods 0.000 description 2
- 238000005304 joining Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000001953 sensory effect Effects 0.000 description 2
- 241000269913 Pseudopleuronectes americanus Species 0.000 description 1
- 241000612182 Rexea solandri Species 0.000 description 1
- 241000719193 Seriola rivoliana Species 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000019552 anatomical structure morphogenesis Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 235000021028 berry Nutrition 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 229910052736 halogen Inorganic materials 0.000 description 1
- 150000002367 halogens Chemical class 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 239000003973 paint Substances 0.000 description 1
- 239000010453 quartz Substances 0.000 description 1
- 238000005057 refrigeration Methods 0.000 description 1
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 1
- YZHUMGUJCQRKBT-UHFFFAOYSA-M sodium chlorate Chemical compound [Na+].[O-]Cl(=O)=O YZHUMGUJCQRKBT-UHFFFAOYSA-M 0.000 description 1
- 239000007921 spray Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A22—BUTCHERING; MEAT TREATMENT; PROCESSING POULTRY OR FISH
- A22C—PROCESSING MEAT, POULTRY, OR FISH
- A22C25/00—Processing fish ; Curing of fish; Stunning of fish by electric current; Investigating fish by optical means
- A22C25/04—Sorting fish; Separating ice from fish packed in ice
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/04—Sorting according to size
- B07C5/10—Sorting according to size measured by light-responsive means
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/04—Sorting according to size
- B07C5/12—Sorting according to size characterised by the application to particular articles, not otherwise provided for
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/34—Sorting according to other particular properties
- B07C5/342—Sorting according to other particular properties according to optical properties, e.g. colour
- B07C5/3422—Sorting according to other particular properties according to optical properties, e.g. colour using video scanning devices, e.g. TV-cameras
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- Food Science & Technology (AREA)
- Zoology (AREA)
- Wood Science & Technology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Biology (AREA)
- Data Mining & Analysis (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Processing Of Meat And Fish (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
A fish sortring apparatus comprising a conveyor belt (1) for carrying one or more rows of fish parallel to its direction of travel, a means (4) for illuminating the fish, a colour video camera (3), a video memory having a number of storage areas connect-ed to the video camera (3), a means for storing the camera derived image values of a fish from the one or more rows in a storage area of the memory, a means for determining edge values of the image and storing them, a means for using the stored edge values to generate shape descriptors the values of which are stored and compared with those already in the memory or the logic of a computer processor (10) associated therewith, and a means for determining the colour and/or light intensity of predetermined areas within the image periphery and storing and comparing values therefor with those stored in the memory. The descriptors are subject to descriminant analysis whereby a signal is generated corresponding to the score derived from the analysis such that fish on the conveyor are deflected into a desired reception area.
Description
~vo 94/09920 2 1 ~ 7 6 0 ~ PCT/GB93/02151 -FISH SORTING MACHINF.
The present invention relates to an apparatus for sorting flP~ible food products, particularly fish, and a method for operating that apparatus. Particularly provided is an apparatus capable of sorting fish into species or groups thereof on the basis of shape and colour.
The provision of apparatus for automatically sorting fish by speri~s~
size and weight, for example on board fishing or factory vessels, makes it possible to automate the collection of data on catch and discards. This has important implications for management of fish stocks and operation of the common fisheries policy.
The need for automatic sorting of fish by species has been discussed by Tayama et al (Reito (Tokyo) Refrigeration Vol. 57 No. 661 (1982) pp. 1146-1150; Shimdate, M. Mitsubishi Denki Hiko (Tokyo). Vol.
56 No. ~ (1982) pp. 44-48. ), Wagner et al (~eb~n! ittelindustrie Vol.30 No.8 (1983) pp. 375-376.;T eben~ ittel;n~ trie Vol. 34 No.1 (1987) pp. 20-23. and Strachan et al ( Pattern Recognition Vol. 23 No. ~ (1990) pp. 539-544.; 'Computer Vision for the fish industry' edited by Pau, L.F. and Olafsson, R., Marcel Decker (1992)).
These researchers have described the shape of fish by length measurements, where length is defined as a horizontal straight line from the tip of the head to the base of the tail; and by width measurements where width is defined as the distance from the top to the bottom of the body measured at a number of equidistant points along the length. They found that they could sort 4 species of fish with a reliability of 95% and 7 species with an accuracy of 90%. A
further suitable apparatus for carrying out simple fish sorting using aspect ratio and area ratio is described in EP 0331390.
For a sorting ~chin~ to be used on board a fishing vessel, up to 30 species of fish may have to be sorted with a reliability of 99%. It W O 94/09920 2 1 ~ 7 6 0 2 PCT/GB93/02151 is therefore necessary to use not only shape but also variables such as colour or shA~in~ to provide additional sorting information.
Colour has been used for image analysis and pattern recognition, for example to identify spray paint caps ( Berry, D.T. Pattern Recognition Letters Vol. 6 (1987) pp. 69-75), to detect the colour codes on resistors ( Bajon et al. 'Identification of multicoloured objects using a vision module' Robot Vision and Sensory Control. 6th Int. Conf. Paris, France. Vol. 10 (1986) pp. 21-30) and to guide a robot to selectively pick up petri dishes (Massen et al Proc. Int.
Conf. Robot Vision and Sensory Controls (1988). pp. 115-122) ).
Colour has also been used to segment images into sets of uniform colour regions ( Tominaga. Proc. 4th Int. Conf. on Pattern Recognition, Cambridge, UK ( 1988) pp. 163-172; Celenk. 'An adaptive ~hine learning algorithm for colour image analysis and processing' Int. Conf. on Manufacturing Systems and Technology, Cambridge, HA, USA (1987) No. 4 pp. 403-412; Kl; nker et al ; SPIE Applications of Artificial Intelligence. 937 (1988) pp. 229-244). and to detect edges in images ( Nevatia. 'A colour edge detector' 3rd Int. Joint Conf. on Pattern Recognition, Coronado, California, USA (1976) pp.
829-837).
The aim of the present invention is to provide an apparatus for automated colour based sorting of irregular objects, eg. fish, which is capable of being integrated with a shape descriptor system, eg apparatus, into a system for sorting fish by species. An improved shape descriptor system is also provided whereby the orientation of the fish with respect to its front and back ends may be automatically asessed and thus all other descriptors related to that.
In order to facilitate comparison of input descriptors with reference descriptors in an automatic apparatus it is necessary to store the latter in some form which can readily be referred to by automatic, particularly electronic, means. Such reference descriptors are thus ~'O 94/09920 21 ~ ~ 6 ~ 2 PCT/GB93/02151 most conveniently stored as values in a computer memory or logic.
There are approximately 30 species of demersal and 5 of pelagic fish landed in Scotland (Department of Agriculture and Fisheries for Scotland 'Scottish Sea Fisheries Statistical Tables 1988'. Crown Copyright (1989) 57 pp. ). A preferred apparatus according to the invention sorts pelagic and demersal fish species and treats them separately. This is convenient because they live in different parts of the water column, demersal living on or near the seabed and pelagic mainly in shoals near the surface, and hence they are ImlikPly to be caught together.
The preferred apparatus of the present invention, and the method for operating it, have been shown to be effective in experiments sorting 5 species of pelagic fish and 18 species of demersal fish; these latter 18 species representing more than 95% of all demersal fish landed in Scotland. The invention offers a rel;Ahility of sorting the 18 species of demersal fish of 100% and of the 5 species of pelagic fish of 98%. This presents a significant impro~ t over all other results so far published, these quoting a sorting accuracy of only 90-95% for 4-7 species of fish.
The present invention provides an apparatus for indicating the type of a fish comprising a means for receiving light from the fish and generating an image of it in the form of a set of values therefrom, a means for storing the set of values in a computer -~, a me_ns for assigning the values to areas of the image, and a means for using these to determine and indicate the type of the fish, characterised in that the apparatus comprises a means for dete.- ining values indicative of the colour of all or some of the areas of the fish and that the type of fish is indicated by means which compares values indicative of colour by area with predetermined such values stored in the memory or a processor associated therewith, these being characteristic of a particular type of fish, and a means which designates the fish as that 21~7~02 4 type having stored values to which the determined values correspond.
Most advantageously the indication is used to direct the fish to a predetermined reception area associated with the type of fish with which it is designated to most closely correspond and the apparatus thus is incorporated as part of an apparatus for automatically sorting fish.
Most preferably the orientation of the fish with respect to which end is that of the head and which is that of the tail is deteL- i n~ . This is conveniently carried out by a novel method which determines the width of the image at two points set a distance between each end and the mid length of the fish image respectively, particularly about one tenth of the total fish image length in from each end, as determined in linear unbent form. Using this method the larger width measurement is indicative of the head end.
rly, the orientation of a flat fish with respect to whether it is top or belly side up may be assessed by interpreting colour as indicative of the eye side surface and lack of colour as indicative of the reverse side. With round fish such as cod, haddock, whiting and saithe the upper half of the fish is always darker than the lower half.
Both head/tail and top/bottom orientation determination offer significant advantage in identifying the fish by the present method.
More preferably the present invention comprises an apparatus for indicating the type of a fish comprising (a) a means for storing one or more sets of values indicative of predetermined length, widths and areas, and the distribution of colour and/or light intensity in these areas, each set characteristic of an image of a particular type of fish (b) means for receiving the image of a fish, the type of ~"0 94/09920 PCT/GB93/02151 -- 21 ~ 7602 which is to be indicated, in a camera;
(c) means for receiving the image from the camera in the form of a set of values and storing these in a memory means;
(d) means for deteL ;n;ng boundary values of the image of the fish;
(e) means for deteL ;n;ng values representing predetermined lengths, widths and/or areas of the image and their ratios, (f) means for determining values indicative of colour of predetermined areas of the image, (g) means for determining which end of the image is representative of the front end and which is representative of the back end of the fish and/or means for deteL ;n;ng which is the upper and which is the lower surface of the fish, and comparing the oriented values from (d) (e) and (f) with correspon~;ng values stored in the means (a) as characteristic of a particular sort of fish;
(h) indicating means which indicates the type of fish to be that having stored values correspon~;ng with the detel ned values.
Most advantageously the indication (h) is used to direct fish to a predetermined sorting area associated with their type.
The means for determining the front and back end preferably uses the length and width data derived by (e) to carry out the method described above based upon width measurements taken at each of two points, one between each end and the mid-length, preferably at about one tenth of the distance from each end. The means for deteL- ;n;ng top and bottom surfaces preferably uses the values indicative of colour of predetermined areas derived by (f) to carry out the method described above whereby the darker or coloured surface is designated the top.
W O 94/09920 PCT/GB93/0215' 21~7602 Thus in a preferred embodiment the present invention provides a sorting apparatus comprising an apparatus as described in either of the embodiments above wherein the fish are presented to a camera, eg.
on a cor,veyor, and their ratios of predetermined length, widths and/or areas, and values indicative of the colour of all or some of the areas, as derived from the fish images produced by that camera, are used to classify the fish, thus generating a control signal which directs the fish to an app.opriate destination. Using preferred '~'; ts of the present invention it is possible to indicate the type of a fish when that fish may be presented in a variety of forms due to its flexibility and defoL ~h; lity, ie. to indicate the type of fish and preferably to direct the fish to an appropriate destination.
A particular form of apparatus of the present invention comprises a conveyor belt for carrying one or more rows of fish, preferably in rows parallel or perpendicular to its direction of travel, a means for illuminating the fish, a colour video camera, a -ly having a number of storage areas connected to the video camera output, a means for storing the camera derived image values of the illuminated fish from the one or more rows in a storage area of the I -Ly, a means for deteL- ;ning the image size and orientation from stored edge values, a means for using the stored edge values to generate shape descriptors the values of which are stored and compared with those already in the memory or the logic of a computer processor associated therewith, and a means for deteL- n;nE the colour of predetermined areas within the image periphery and storing and comparing values therefor with those stored in the memory or the logic.
One suitable such means for deteL- n; nE shape descriptors and dividingthe image up into length, width and area values is described in EP
0331390, wherein values are stored and transferred between bit planes.
Typically apparatus of the present invention will comprise a colour video camera with red, green and blue signal outputs connected to a 'O 94/09920 21 ~ 7 ~ 0 2 P ~ /GB93/02151 computer memory whereby a pattern of pixels correspon~ing to the image to be analysed can be produced. This memory is connected to a processor for generating the finite elements of aspect and area ratios and the average red, green and blue values of selected parts of the image. These elements, or descriptors, are compared with values previously generated, eg. from calibration of the apparatus using a number of fish of known type, ie. fish of known species, or built into the computer logic, and a score generated indicative of whether or not the fish is of a given type, eg. species.
It is preferred to determine the length, width and/or area values of the fish by back-lighting it in the image receiving step. This is conveniently achieved by use of a light source mounted below the fish as it is presented to the means for receiving the image, ie.
the camera. Where a conveyor belt is used this is conveniently made of a material which transmits at least a proportion of light emitted from a source placed below it such that back-lighting can be achieved thereby providing a distinct silhouette.
It is preferred to determine the colour values of the fish by front-lighting the object in the image-receiving step. This is particularly advantageously carried out using diffuse light generated by directing light from one or more light sources onto a reflective surface from which it passes onto the top of the object. Conveniently the reflective surface is the inside a a housing in which the image receiving means, eg. video camera, is mounted. Preferably the reflective surface is of white colouration. The preferred method of operating the apparatus of the invention calibrates the camera outputs to no~ e them before operation.
It will be realised by those skilled in the art that signals generated by the computer can readily be used to operate a series of deflectors on a conveyor belt to deflect a fish to its determined sorting destination where all fish of that type will be deflected to.
W O 94/09920 PCT/GB93/0215' 21il~ 602 8 TABLE 1: The groups of Aspect ratio and Area Ratio that are used to classify fish into various groups according to the method of the invention for use with the apparatus of the invention, including the two main groups (Groups 1 and 2) requiring colour analysis Fi.~h of Fi.~h of All Fish A.~pect rnt~o Aren rntio >0.40 >0.40 (Group 1 herein) Angler fish Angler fish Dab Catfish Dab Flounder Cod Flounder Lemon Sole Coley Lemon Sole Megrim Dab Megrim Plaice Flounder ~ Plaice Redfish Haddock Redfish Sole Hake Skate \ \ Witch Lemon Sole Sole \ ~
Ling Witch \ 0.20-0.40 Megrim \ Angler fish Plaice 0.20-0.40 Red mullet \ (Croup Z herein) ~
Redfish \ Catfish <0.20 Skate ~ Cod Skate Sole Coley Witch Haddock Whiting Hake Red mullet Redfish Whiting1 >0.80 < Z / Ling Hake \ <0.80 Ling Hake A method for operating the apparatus of the present invention is also provided based upon use of multivariate analysis, particularly discr;~;n~nt analysis and is that disclosed in the Examples described below, although other multivariate data processing technoques such as neural networking may equally be applied with success. The apparatus and method of the present invention will now be illustrated with reference the non-limiting examples, Examples l to 3 below, further -'; ts falling within the scope of the claims will occur to those skilled in the art in the light of these. Figures l to 8 are provided to illustrate aspects of the Examples.
FIGURES
Figure l shows a shape grid constructed for a whiting in two different positions, the grid being shown superimposed upon the fish silhouette.
Figure 2 shows a grid suitable for a flatfish as shown superimposed upon the silhouette of a lemon sole (upper image) and a megrim (lower image).
Figure 3 shows the position of one particular significant coloured area (shown as a light area) in the grid for a flounder, a group l fish by the method of Table l.
Figure 4 shows the position of some particular significant coloured areas (shown as light areas) in the grids of catfish (upper silhouette) and cod (lower silhouette), group 2 fish by the scheme of Table l.
Figure 5 shows the position of some particular significant coloured areas (shown as light areas) in the grids of herring (upper silhouette) and sprat (lower silhouette), pelagic fish.
Figure 6 shows a flow chart setting out the stages in determination of the type of fish from their images, backlit and frontlit, as provided W O 94/09920 P ~ /GB93/021'' 214760~
by an embodiment of apparatus of the invention and a method of operating it.
Figure 7 shows a diagrammatic representation of a perspective view of a fish sorting apparatus of the present invention.
Figure 8 shows a diagrammatic representation of the arrangement of the means for dete-- n;nF values indicative of colour of areas of the object in a preferred embodiment of the invention and its relation~hip to the other parts of the apparatus of Figure 7.
EXAMPT.T~ 1: App~r~tl-~ of the invention ~n~ ~ethod of ODer~ting it.
A fish sorting apparatus according to the present invention (see Figure 7) was provided comprising quartz halogen lamps (4) GEC 300 W
power with a colour temperature of 2854 K, placed in a housing (2) over a partially transparent conveyor belt (1). The interior of the housing was matt white in colour such that the light from the lamps (4) was reflected from its walls and ceiling to give the diffuse lighting that is required to reduce the amount of specular reflection from the glossy skin of the fish.
A video camera (3), Sony DXC 325PK three chip colour video camera, was suspended from the top of the housing and before operation of the apparatus the automatic gain control was switched off. The images of the fish passed before the camera were taken both backlit using a lightbox (5) placed between the runs of the conve~or to give the silhouette, and frontlit to give the colour. Red, Green and Blue values were fed from the camera to a computer arrangement (described below and in Figure 8) which was used to determine the various values from which descriminant analysis was used to determine fish type (species). Outputs from the computer (9) were used to selectively operate deflector elements (7) on a diverter conve~ur (6) such that fish that had been typed were deflected into an a~ u~Liate one of "O 94/09920 21~ 7 ~ 0 2 P ~ /GB93/02151 a number of species bins (8).
The colour values were obtained from the red, green and blue (RGB) outputs from the video camera. These signals were then digitised to eight bits per colour using a frame grabbing board (T ae~ne Technology Inc. or Sprynt Colour Input Board) and then stored on a computer workstation (486PC Sun 3-160C) where they were processed by an i860 CPU using algorithms written in the C pro~l- ne language.
Colour Calibration: The black (R(b), G(b) and B(b)) and white (R(w), G(w) and B(w) camera output signal levels were obtained by lens capping and use of a reference white tile with the camera aperture set at f4 respectively. The red, green and blue (Rp, Gp, Bp) camera outputs were then normalised according to the black and white levels, using the following equations:
R = R~ - R(b) x 100% (l) R(w) - R(b) G = G~ - G(b) x 100% (2) G(w) - G(b) B = Bp - B(b) x 100% (3) B(w) - B(b) The calibration was done once a day using a Macbeth colour chart.
Simple ShAne Descri~tors. Two descriptors were used for crudely sorting the fish. The first was the aspect ratio (As), As = ~ (4) where w is the ~ width of the fish and l is the length of the fish from its nose to the end of its tail.
The second was the area ratio (Ar) Ar = - (5) Ab where Ar is the area of the front half of the fish, from the head to the midpoint of the fish, and Ab is the area of the back half of the fish, from the tail to the midpoint of the fish.
Position Reference Svstem An~ ShAne DescriDtors: Flatfish are fish with large aspect ratios (As > 0.40) and do not bend very much. But fish with small values (As < 0.40) bend and deform relatively easily (Webb and Weihs, 'Fish Biomechanics', Praeger Puhlish~rs, New York, USA. (1983)). To sort fish by species it is useful to analyse, for example, how the width changes along its length and the colouration across the fish. To do this a position reference coordinate system should be estAhl;~hed which can acc ~dAte fish deformations and bendlng .
Advantageously, the orientation of the fish with respect to which end is that of the head and which is that of the tail was determined.
This was conveniently carried out by dete. ;n;ng the width of the image at a distance of about one tenth of the total image length in from each end. Using this method the larger measurement is indicative of the head end. sir; 1 Arly, the orientation of a flat fish with respect whether it was top or belly side up was assessed by interpreting colour as indicative of the eye side surface and lack of colour as indicative of the reverse side. With round fish such as cod, haddock, whiting and saithe the upper half of the fish is always darker than the lower half and this information is also usefully used '~'~ 94/09920 21 ~ 7 6 ~ % P ~ /GB93/02151 in orienting fish images.
For esch fish image the computer processor was used to construct a grid, typically consisting 36 quadrilateral elements as in Fig 1.
Strachan et al (J. Photographic Sci. 1990) have named this grid the 'shape grid' because it is derived from the shape of the fish. Shown is the shape grid for a whiting in two different deformation states showing that the grids bend with the fish.
For each fish type a shape grid was constructed as follows:
(i) The image of the fish silhouette was thresholded, made binary, the principal axis was found and the fish silhouette oriented on it horizontally (see Strachan: Pattern Recognition (1990) as above), (ii) The width of the fish was determined at each point along its length by dropping vertical lines from its top edge to its bottom edge and the locus of the midpoints of these lines produced the central line of the grid, (iii) From the width profile of the fish along its length the tip of the head and the narrowest point of the tail stalk were detected, (iv) The central line was a~ o~imated at 10 segments of equal length joining the tip of the head to the narrowest point of the tail stalk (v) The normals of the central line were drawn from each of the points joining these segments to both the upper and lower edge of the fish image, (vi) Each normal (both upper and lower) was split into two equal segments, and (vii) All of these segments were connected to produce the shape grid.
2147~02 It can be seen that such a grid can be constructed to bend with the fish and thus is suitably constructed by a computer from values in a memory, ie. data in the form of pixels, representative of the image of a bent fish. The bent grid may be transformed into a straight grid or vice versa by processing with algorithms using method of Bookstein, Transformations of quadrilaterals, tensor fields, and morphogenesis.
In P L Anton~ll; (Ed.) Mathematical Essays on Growth and the F - ~ellce of Form. University of Alberta Press, Edmonton, Alberta (1985).
Such a grid is capable of overlapping itself and hence a different sort of grid is required for fish with large aspect ratios, ie.
flatfish. The grid shown in Fig 2 is suitable for flatfish. It is constructed using vertical lines instead of normals to the central symmetry line. This grid will be poor at modelling the bending of fish but as has been mentioned already flatfish bend very little and hence this problem can be ignored.
From either type of shape grid a set of descriptors can be obtained to describe the shape of the fish. These were the 10 widths of the fish defined by the grids and the length of the fish from its nose to the apex of its tail. These shape descriptors were used to sort the fish by species.
Colour Descriptors: For the tail and the nose (which in the present example consists of the front four shape grid elements) and for each of the 36 other elements of the shape grid (or the simple shape grid), the average R, G and B values are determined. This produces 114 variables (36 elements plus nose and tail with average R, G, B in each = 38 x 3 = 114) which can be used to sort the fish by species.
Discriminant An~l ysis: Discriminant analysis (Nie et al 'Statistical package for the social sciences, 2nd ed., McGraw-Hill, New York, U.S.A. (1975). ) is used to process the shape descriptor and the colour descriptor data derived from the image in the memory store, ~~'0 94/09920 21 ~ 7 6 0 2 PCT/GB93/02151 employing linear combinations of vsriables to distinguish between the different species of fish.
The use of discriminant analysis requires the linesr combination of the varisbles that best distinguishes between the different species of fish to be found and used by the processor thst csrries out the generation of the descriptors from the colour imsge. A stepwise method wss used to generste discriminant functions by introducing one vsriable at a time. If the new function satisfied the discriminant analysis criterion then it wss sccepted. If not, it W8S rejected along with the last variable introduced.
At the end of this process the resulting clsssification coefficients ClJ (Fisher's linear discriminant functions) were obtained. Cl, the classification score for species group i, is given by a linesr combination of Ci~'s:
jmax Cl = ~ ClJ Q(;) ~ C1O (6) j=l where i = 1,2 ... n (n = no of species of fish), C1O is a constant and the Q(j)'s are the raw values of the variables.
The variables that were used in Examples l to 3 are:
l. The eleven shape descriptors (jmax = ll) 2. The R, G, B aversge vslues of the nose and front third shspe grid elements of the fish (jmax = 39) 3. The R, G, ~ average values of the middle third shape grid elements of the fish (jmax = 36) W O 94/09920 2 1 4 7 S ~ ~ PCT/GB93/021~
The present invention relates to an apparatus for sorting flP~ible food products, particularly fish, and a method for operating that apparatus. Particularly provided is an apparatus capable of sorting fish into species or groups thereof on the basis of shape and colour.
The provision of apparatus for automatically sorting fish by speri~s~
size and weight, for example on board fishing or factory vessels, makes it possible to automate the collection of data on catch and discards. This has important implications for management of fish stocks and operation of the common fisheries policy.
The need for automatic sorting of fish by species has been discussed by Tayama et al (Reito (Tokyo) Refrigeration Vol. 57 No. 661 (1982) pp. 1146-1150; Shimdate, M. Mitsubishi Denki Hiko (Tokyo). Vol.
56 No. ~ (1982) pp. 44-48. ), Wagner et al (~eb~n! ittelindustrie Vol.30 No.8 (1983) pp. 375-376.;T eben~ ittel;n~ trie Vol. 34 No.1 (1987) pp. 20-23. and Strachan et al ( Pattern Recognition Vol. 23 No. ~ (1990) pp. 539-544.; 'Computer Vision for the fish industry' edited by Pau, L.F. and Olafsson, R., Marcel Decker (1992)).
These researchers have described the shape of fish by length measurements, where length is defined as a horizontal straight line from the tip of the head to the base of the tail; and by width measurements where width is defined as the distance from the top to the bottom of the body measured at a number of equidistant points along the length. They found that they could sort 4 species of fish with a reliability of 95% and 7 species with an accuracy of 90%. A
further suitable apparatus for carrying out simple fish sorting using aspect ratio and area ratio is described in EP 0331390.
For a sorting ~chin~ to be used on board a fishing vessel, up to 30 species of fish may have to be sorted with a reliability of 99%. It W O 94/09920 2 1 ~ 7 6 0 2 PCT/GB93/02151 is therefore necessary to use not only shape but also variables such as colour or shA~in~ to provide additional sorting information.
Colour has been used for image analysis and pattern recognition, for example to identify spray paint caps ( Berry, D.T. Pattern Recognition Letters Vol. 6 (1987) pp. 69-75), to detect the colour codes on resistors ( Bajon et al. 'Identification of multicoloured objects using a vision module' Robot Vision and Sensory Control. 6th Int. Conf. Paris, France. Vol. 10 (1986) pp. 21-30) and to guide a robot to selectively pick up petri dishes (Massen et al Proc. Int.
Conf. Robot Vision and Sensory Controls (1988). pp. 115-122) ).
Colour has also been used to segment images into sets of uniform colour regions ( Tominaga. Proc. 4th Int. Conf. on Pattern Recognition, Cambridge, UK ( 1988) pp. 163-172; Celenk. 'An adaptive ~hine learning algorithm for colour image analysis and processing' Int. Conf. on Manufacturing Systems and Technology, Cambridge, HA, USA (1987) No. 4 pp. 403-412; Kl; nker et al ; SPIE Applications of Artificial Intelligence. 937 (1988) pp. 229-244). and to detect edges in images ( Nevatia. 'A colour edge detector' 3rd Int. Joint Conf. on Pattern Recognition, Coronado, California, USA (1976) pp.
829-837).
The aim of the present invention is to provide an apparatus for automated colour based sorting of irregular objects, eg. fish, which is capable of being integrated with a shape descriptor system, eg apparatus, into a system for sorting fish by species. An improved shape descriptor system is also provided whereby the orientation of the fish with respect to its front and back ends may be automatically asessed and thus all other descriptors related to that.
In order to facilitate comparison of input descriptors with reference descriptors in an automatic apparatus it is necessary to store the latter in some form which can readily be referred to by automatic, particularly electronic, means. Such reference descriptors are thus ~'O 94/09920 21 ~ ~ 6 ~ 2 PCT/GB93/02151 most conveniently stored as values in a computer memory or logic.
There are approximately 30 species of demersal and 5 of pelagic fish landed in Scotland (Department of Agriculture and Fisheries for Scotland 'Scottish Sea Fisheries Statistical Tables 1988'. Crown Copyright (1989) 57 pp. ). A preferred apparatus according to the invention sorts pelagic and demersal fish species and treats them separately. This is convenient because they live in different parts of the water column, demersal living on or near the seabed and pelagic mainly in shoals near the surface, and hence they are ImlikPly to be caught together.
The preferred apparatus of the present invention, and the method for operating it, have been shown to be effective in experiments sorting 5 species of pelagic fish and 18 species of demersal fish; these latter 18 species representing more than 95% of all demersal fish landed in Scotland. The invention offers a rel;Ahility of sorting the 18 species of demersal fish of 100% and of the 5 species of pelagic fish of 98%. This presents a significant impro~ t over all other results so far published, these quoting a sorting accuracy of only 90-95% for 4-7 species of fish.
The present invention provides an apparatus for indicating the type of a fish comprising a means for receiving light from the fish and generating an image of it in the form of a set of values therefrom, a means for storing the set of values in a computer -~, a me_ns for assigning the values to areas of the image, and a means for using these to determine and indicate the type of the fish, characterised in that the apparatus comprises a means for dete.- ining values indicative of the colour of all or some of the areas of the fish and that the type of fish is indicated by means which compares values indicative of colour by area with predetermined such values stored in the memory or a processor associated therewith, these being characteristic of a particular type of fish, and a means which designates the fish as that 21~7~02 4 type having stored values to which the determined values correspond.
Most advantageously the indication is used to direct the fish to a predetermined reception area associated with the type of fish with which it is designated to most closely correspond and the apparatus thus is incorporated as part of an apparatus for automatically sorting fish.
Most preferably the orientation of the fish with respect to which end is that of the head and which is that of the tail is deteL- i n~ . This is conveniently carried out by a novel method which determines the width of the image at two points set a distance between each end and the mid length of the fish image respectively, particularly about one tenth of the total fish image length in from each end, as determined in linear unbent form. Using this method the larger width measurement is indicative of the head end.
rly, the orientation of a flat fish with respect to whether it is top or belly side up may be assessed by interpreting colour as indicative of the eye side surface and lack of colour as indicative of the reverse side. With round fish such as cod, haddock, whiting and saithe the upper half of the fish is always darker than the lower half.
Both head/tail and top/bottom orientation determination offer significant advantage in identifying the fish by the present method.
More preferably the present invention comprises an apparatus for indicating the type of a fish comprising (a) a means for storing one or more sets of values indicative of predetermined length, widths and areas, and the distribution of colour and/or light intensity in these areas, each set characteristic of an image of a particular type of fish (b) means for receiving the image of a fish, the type of ~"0 94/09920 PCT/GB93/02151 -- 21 ~ 7602 which is to be indicated, in a camera;
(c) means for receiving the image from the camera in the form of a set of values and storing these in a memory means;
(d) means for deteL ;n;ng boundary values of the image of the fish;
(e) means for deteL ;n;ng values representing predetermined lengths, widths and/or areas of the image and their ratios, (f) means for determining values indicative of colour of predetermined areas of the image, (g) means for determining which end of the image is representative of the front end and which is representative of the back end of the fish and/or means for deteL ;n;ng which is the upper and which is the lower surface of the fish, and comparing the oriented values from (d) (e) and (f) with correspon~;ng values stored in the means (a) as characteristic of a particular sort of fish;
(h) indicating means which indicates the type of fish to be that having stored values correspon~;ng with the detel ned values.
Most advantageously the indication (h) is used to direct fish to a predetermined sorting area associated with their type.
The means for determining the front and back end preferably uses the length and width data derived by (e) to carry out the method described above based upon width measurements taken at each of two points, one between each end and the mid-length, preferably at about one tenth of the distance from each end. The means for deteL- ;n;ng top and bottom surfaces preferably uses the values indicative of colour of predetermined areas derived by (f) to carry out the method described above whereby the darker or coloured surface is designated the top.
W O 94/09920 PCT/GB93/0215' 21~7602 Thus in a preferred embodiment the present invention provides a sorting apparatus comprising an apparatus as described in either of the embodiments above wherein the fish are presented to a camera, eg.
on a cor,veyor, and their ratios of predetermined length, widths and/or areas, and values indicative of the colour of all or some of the areas, as derived from the fish images produced by that camera, are used to classify the fish, thus generating a control signal which directs the fish to an app.opriate destination. Using preferred '~'; ts of the present invention it is possible to indicate the type of a fish when that fish may be presented in a variety of forms due to its flexibility and defoL ~h; lity, ie. to indicate the type of fish and preferably to direct the fish to an appropriate destination.
A particular form of apparatus of the present invention comprises a conveyor belt for carrying one or more rows of fish, preferably in rows parallel or perpendicular to its direction of travel, a means for illuminating the fish, a colour video camera, a -ly having a number of storage areas connected to the video camera output, a means for storing the camera derived image values of the illuminated fish from the one or more rows in a storage area of the I -Ly, a means for deteL- ;ning the image size and orientation from stored edge values, a means for using the stored edge values to generate shape descriptors the values of which are stored and compared with those already in the memory or the logic of a computer processor associated therewith, and a means for deteL- n;nE the colour of predetermined areas within the image periphery and storing and comparing values therefor with those stored in the memory or the logic.
One suitable such means for deteL- n; nE shape descriptors and dividingthe image up into length, width and area values is described in EP
0331390, wherein values are stored and transferred between bit planes.
Typically apparatus of the present invention will comprise a colour video camera with red, green and blue signal outputs connected to a 'O 94/09920 21 ~ 7 ~ 0 2 P ~ /GB93/02151 computer memory whereby a pattern of pixels correspon~ing to the image to be analysed can be produced. This memory is connected to a processor for generating the finite elements of aspect and area ratios and the average red, green and blue values of selected parts of the image. These elements, or descriptors, are compared with values previously generated, eg. from calibration of the apparatus using a number of fish of known type, ie. fish of known species, or built into the computer logic, and a score generated indicative of whether or not the fish is of a given type, eg. species.
It is preferred to determine the length, width and/or area values of the fish by back-lighting it in the image receiving step. This is conveniently achieved by use of a light source mounted below the fish as it is presented to the means for receiving the image, ie.
the camera. Where a conveyor belt is used this is conveniently made of a material which transmits at least a proportion of light emitted from a source placed below it such that back-lighting can be achieved thereby providing a distinct silhouette.
It is preferred to determine the colour values of the fish by front-lighting the object in the image-receiving step. This is particularly advantageously carried out using diffuse light generated by directing light from one or more light sources onto a reflective surface from which it passes onto the top of the object. Conveniently the reflective surface is the inside a a housing in which the image receiving means, eg. video camera, is mounted. Preferably the reflective surface is of white colouration. The preferred method of operating the apparatus of the invention calibrates the camera outputs to no~ e them before operation.
It will be realised by those skilled in the art that signals generated by the computer can readily be used to operate a series of deflectors on a conveyor belt to deflect a fish to its determined sorting destination where all fish of that type will be deflected to.
W O 94/09920 PCT/GB93/0215' 21il~ 602 8 TABLE 1: The groups of Aspect ratio and Area Ratio that are used to classify fish into various groups according to the method of the invention for use with the apparatus of the invention, including the two main groups (Groups 1 and 2) requiring colour analysis Fi.~h of Fi.~h of All Fish A.~pect rnt~o Aren rntio >0.40 >0.40 (Group 1 herein) Angler fish Angler fish Dab Catfish Dab Flounder Cod Flounder Lemon Sole Coley Lemon Sole Megrim Dab Megrim Plaice Flounder ~ Plaice Redfish Haddock Redfish Sole Hake Skate \ \ Witch Lemon Sole Sole \ ~
Ling Witch \ 0.20-0.40 Megrim \ Angler fish Plaice 0.20-0.40 Red mullet \ (Croup Z herein) ~
Redfish \ Catfish <0.20 Skate ~ Cod Skate Sole Coley Witch Haddock Whiting Hake Red mullet Redfish Whiting1 >0.80 < Z / Ling Hake \ <0.80 Ling Hake A method for operating the apparatus of the present invention is also provided based upon use of multivariate analysis, particularly discr;~;n~nt analysis and is that disclosed in the Examples described below, although other multivariate data processing technoques such as neural networking may equally be applied with success. The apparatus and method of the present invention will now be illustrated with reference the non-limiting examples, Examples l to 3 below, further -'; ts falling within the scope of the claims will occur to those skilled in the art in the light of these. Figures l to 8 are provided to illustrate aspects of the Examples.
FIGURES
Figure l shows a shape grid constructed for a whiting in two different positions, the grid being shown superimposed upon the fish silhouette.
Figure 2 shows a grid suitable for a flatfish as shown superimposed upon the silhouette of a lemon sole (upper image) and a megrim (lower image).
Figure 3 shows the position of one particular significant coloured area (shown as a light area) in the grid for a flounder, a group l fish by the method of Table l.
Figure 4 shows the position of some particular significant coloured areas (shown as light areas) in the grids of catfish (upper silhouette) and cod (lower silhouette), group 2 fish by the scheme of Table l.
Figure 5 shows the position of some particular significant coloured areas (shown as light areas) in the grids of herring (upper silhouette) and sprat (lower silhouette), pelagic fish.
Figure 6 shows a flow chart setting out the stages in determination of the type of fish from their images, backlit and frontlit, as provided W O 94/09920 P ~ /GB93/021'' 214760~
by an embodiment of apparatus of the invention and a method of operating it.
Figure 7 shows a diagrammatic representation of a perspective view of a fish sorting apparatus of the present invention.
Figure 8 shows a diagrammatic representation of the arrangement of the means for dete-- n;nF values indicative of colour of areas of the object in a preferred embodiment of the invention and its relation~hip to the other parts of the apparatus of Figure 7.
EXAMPT.T~ 1: App~r~tl-~ of the invention ~n~ ~ethod of ODer~ting it.
A fish sorting apparatus according to the present invention (see Figure 7) was provided comprising quartz halogen lamps (4) GEC 300 W
power with a colour temperature of 2854 K, placed in a housing (2) over a partially transparent conveyor belt (1). The interior of the housing was matt white in colour such that the light from the lamps (4) was reflected from its walls and ceiling to give the diffuse lighting that is required to reduce the amount of specular reflection from the glossy skin of the fish.
A video camera (3), Sony DXC 325PK three chip colour video camera, was suspended from the top of the housing and before operation of the apparatus the automatic gain control was switched off. The images of the fish passed before the camera were taken both backlit using a lightbox (5) placed between the runs of the conve~or to give the silhouette, and frontlit to give the colour. Red, Green and Blue values were fed from the camera to a computer arrangement (described below and in Figure 8) which was used to determine the various values from which descriminant analysis was used to determine fish type (species). Outputs from the computer (9) were used to selectively operate deflector elements (7) on a diverter conve~ur (6) such that fish that had been typed were deflected into an a~ u~Liate one of "O 94/09920 21~ 7 ~ 0 2 P ~ /GB93/02151 a number of species bins (8).
The colour values were obtained from the red, green and blue (RGB) outputs from the video camera. These signals were then digitised to eight bits per colour using a frame grabbing board (T ae~ne Technology Inc. or Sprynt Colour Input Board) and then stored on a computer workstation (486PC Sun 3-160C) where they were processed by an i860 CPU using algorithms written in the C pro~l- ne language.
Colour Calibration: The black (R(b), G(b) and B(b)) and white (R(w), G(w) and B(w) camera output signal levels were obtained by lens capping and use of a reference white tile with the camera aperture set at f4 respectively. The red, green and blue (Rp, Gp, Bp) camera outputs were then normalised according to the black and white levels, using the following equations:
R = R~ - R(b) x 100% (l) R(w) - R(b) G = G~ - G(b) x 100% (2) G(w) - G(b) B = Bp - B(b) x 100% (3) B(w) - B(b) The calibration was done once a day using a Macbeth colour chart.
Simple ShAne Descri~tors. Two descriptors were used for crudely sorting the fish. The first was the aspect ratio (As), As = ~ (4) where w is the ~ width of the fish and l is the length of the fish from its nose to the end of its tail.
The second was the area ratio (Ar) Ar = - (5) Ab where Ar is the area of the front half of the fish, from the head to the midpoint of the fish, and Ab is the area of the back half of the fish, from the tail to the midpoint of the fish.
Position Reference Svstem An~ ShAne DescriDtors: Flatfish are fish with large aspect ratios (As > 0.40) and do not bend very much. But fish with small values (As < 0.40) bend and deform relatively easily (Webb and Weihs, 'Fish Biomechanics', Praeger Puhlish~rs, New York, USA. (1983)). To sort fish by species it is useful to analyse, for example, how the width changes along its length and the colouration across the fish. To do this a position reference coordinate system should be estAhl;~hed which can acc ~dAte fish deformations and bendlng .
Advantageously, the orientation of the fish with respect to which end is that of the head and which is that of the tail was determined.
This was conveniently carried out by dete. ;n;ng the width of the image at a distance of about one tenth of the total image length in from each end. Using this method the larger measurement is indicative of the head end. sir; 1 Arly, the orientation of a flat fish with respect whether it was top or belly side up was assessed by interpreting colour as indicative of the eye side surface and lack of colour as indicative of the reverse side. With round fish such as cod, haddock, whiting and saithe the upper half of the fish is always darker than the lower half and this information is also usefully used '~'~ 94/09920 21 ~ 7 6 ~ % P ~ /GB93/02151 in orienting fish images.
For esch fish image the computer processor was used to construct a grid, typically consisting 36 quadrilateral elements as in Fig 1.
Strachan et al (J. Photographic Sci. 1990) have named this grid the 'shape grid' because it is derived from the shape of the fish. Shown is the shape grid for a whiting in two different deformation states showing that the grids bend with the fish.
For each fish type a shape grid was constructed as follows:
(i) The image of the fish silhouette was thresholded, made binary, the principal axis was found and the fish silhouette oriented on it horizontally (see Strachan: Pattern Recognition (1990) as above), (ii) The width of the fish was determined at each point along its length by dropping vertical lines from its top edge to its bottom edge and the locus of the midpoints of these lines produced the central line of the grid, (iii) From the width profile of the fish along its length the tip of the head and the narrowest point of the tail stalk were detected, (iv) The central line was a~ o~imated at 10 segments of equal length joining the tip of the head to the narrowest point of the tail stalk (v) The normals of the central line were drawn from each of the points joining these segments to both the upper and lower edge of the fish image, (vi) Each normal (both upper and lower) was split into two equal segments, and (vii) All of these segments were connected to produce the shape grid.
2147~02 It can be seen that such a grid can be constructed to bend with the fish and thus is suitably constructed by a computer from values in a memory, ie. data in the form of pixels, representative of the image of a bent fish. The bent grid may be transformed into a straight grid or vice versa by processing with algorithms using method of Bookstein, Transformations of quadrilaterals, tensor fields, and morphogenesis.
In P L Anton~ll; (Ed.) Mathematical Essays on Growth and the F - ~ellce of Form. University of Alberta Press, Edmonton, Alberta (1985).
Such a grid is capable of overlapping itself and hence a different sort of grid is required for fish with large aspect ratios, ie.
flatfish. The grid shown in Fig 2 is suitable for flatfish. It is constructed using vertical lines instead of normals to the central symmetry line. This grid will be poor at modelling the bending of fish but as has been mentioned already flatfish bend very little and hence this problem can be ignored.
From either type of shape grid a set of descriptors can be obtained to describe the shape of the fish. These were the 10 widths of the fish defined by the grids and the length of the fish from its nose to the apex of its tail. These shape descriptors were used to sort the fish by species.
Colour Descriptors: For the tail and the nose (which in the present example consists of the front four shape grid elements) and for each of the 36 other elements of the shape grid (or the simple shape grid), the average R, G and B values are determined. This produces 114 variables (36 elements plus nose and tail with average R, G, B in each = 38 x 3 = 114) which can be used to sort the fish by species.
Discriminant An~l ysis: Discriminant analysis (Nie et al 'Statistical package for the social sciences, 2nd ed., McGraw-Hill, New York, U.S.A. (1975). ) is used to process the shape descriptor and the colour descriptor data derived from the image in the memory store, ~~'0 94/09920 21 ~ 7 6 0 2 PCT/GB93/02151 employing linear combinations of vsriables to distinguish between the different species of fish.
The use of discriminant analysis requires the linesr combination of the varisbles that best distinguishes between the different species of fish to be found and used by the processor thst csrries out the generation of the descriptors from the colour imsge. A stepwise method wss used to generste discriminant functions by introducing one vsriable at a time. If the new function satisfied the discriminant analysis criterion then it wss sccepted. If not, it W8S rejected along with the last variable introduced.
At the end of this process the resulting clsssification coefficients ClJ (Fisher's linear discriminant functions) were obtained. Cl, the classification score for species group i, is given by a linesr combination of Ci~'s:
jmax Cl = ~ ClJ Q(;) ~ C1O (6) j=l where i = 1,2 ... n (n = no of species of fish), C1O is a constant and the Q(j)'s are the raw values of the variables.
The variables that were used in Examples l to 3 are:
l. The eleven shape descriptors (jmax = ll) 2. The R, G, B aversge vslues of the nose and front third shspe grid elements of the fish (jmax = 39) 3. The R, G, ~ average values of the middle third shape grid elements of the fish (jmax = 36) W O 94/09920 2 1 4 7 S ~ ~ PCT/GB93/021~
4. The R, G, B average values of the tail and tail third shape grid elements of the fish (jmax = 39) The colour descriptors were split into three subgroups because in any technique using multivariate analysis the analysis performs better if the number of variables is reduced (Digby, P.G.N. and Kempton, R.A.
'Multivariate Analysis of Ecological Communities'. Ch~ ~n and HP11, London, F.ngl Pnd ( 1987).
Since there were n species of fish, every new fish to be sorted for species was given n scores, one from each classification function.
Fish were sorted as the species whose function had the highest score.
For the discriminant analysis the fish have to be split into two sets (the calibration and the test set). The species of the calibration set fish along with the variables is entered into the processor and from these the discriminant analysis algorithm produces the Fisher classification coefficients. The variables of the test set of fish are then introduced to the processor and this determines the species of these fish using the Fisher linear discriminant functions.
The arrangement of the processor with respect to the R, G, B input from the video camera and its associated equipment is shown in Figure 8. The R, G, B analog inputs from the camera are fed to respective 8 bit analog to digital converters connected in turn to look up tables and 2 megabytes of FIF0 store. This store is accessed by a 64 bit data bus further connected to a video random access memory and a video monitor, an 8 megabyte DRAM and the central processing unit (CPU).
The CPU has outputs which control the deflectors of the divertor col1veyor.
In experiments carried out using apparatus configured as described in Example l and in Figures 5 to 7 there were at most 35 fish of each species, hence where possible 20 fish of each species are used as the calibration set. This is because the larger the calibration set the ~~~O 94/09920 ~ 1 ~ 7 S 0 2 P ~ /GB93/02151 better the discriminant analysis performs.
The aforesaid apparatus, with the processor pro l- cd to carry out descriminant analyis as described was provided with fish of either Demersal (Example 2) or Pelagic (Example 3) types, these not usually requiring sorting together.
EXAMPLE 2: A~aratus for sorting Desmersal fish.
The apparatus was set up such that the computer processor first sorted images according to aspect ratio As and then according to their area ratio Ar (see Table l in introduction ). This identified angler fish, skate, ling and hake. There remained two groups of fish which required further sorting. Group l consisted mainly of flat fish and some round fish which have large aspect ratios. Group 2 consisted exclusively of round fish. 18 fish species were sorted in total.
For Group l the simple shape grid was generated for each of the species of fish from which shape and colour descriptors were generated (example position of colour varying area shown in Fig 3) and these were subjected to discriminant analysis. For the calibration set the discriminant analysis sorted all of the fish (135 fish) correctly ie.
lO0~ sorting reliability for the shape descriptors and the 3 sets of colour descriptors. For the test set of fish the shape descriptors had a sorting reliability of 94% (92 out of 98), the front third colour descriptors had a sorting reliability of 95% (93 out ot 98), the middle third colour descriptors had a sorting reliability of 95%
(93 out of 98) and the tail third colour descriptors had a sorting reliability of 94% (92 out of 98). These four sets of results were combined by simply classifying a fish as the one most preferred by - adding the results together. In doing this the sorting reliability obtained was 100% (98 out of 98).
For the Group 2 fish the shape grid was used. From this the shape and colour descriptors were obtained (examples of colour varying areas shown in Fig 4) and these were then subjected to discriminant analysis. For the calibration set the discriminant analysis sorted all of the fish (117 fish) correctly, ie. a 100% sorting reliability for the shape descriptors and the 3 sets of colour descriptors. For the test set the shape descriptors had a sorting reliability of 90%
(69 out of 77), the front third colour descriptors had a sorting reliability of 96% (74 out of 77), the middle third colour descriptors had a sorting reliability of 100% (77 out of 77) and the tail third colour descriptors had a sorting reliability of 100% (77 out of 77).
When these four sets of results were combined as before a sorting reliability of 100% (77 out of 77) was obtained.
Hence the 18 species of demersal fish used in these experiments were sorted with a reliability of 100% (133/133 test fish).
~xamvle ~: Ap~aratus for sorting Pel~gic fish.
The five main species of pelagic fish have similar values of Ar and As therefore the procedure of Table 3 does not work on pelagic species.
Shape grid were generated for all of the pelagic fish and from these the shape and colour descriptors were calculated (see examples of position of colour varying areas Flg 5) and subjected to discriminant analysis. For the calibration set the discriminant analysis sorted all of the fish (80 fish) correctly ie a 100% sorting reliability for the shape descriptors and the 3 (front, middle and tail) sets of colour descriptors. For the test set of fish the shape descriptors had a sorting reliability of 98% (55 out of 56), the front third colour descriptors had a sorting reliability of 100% (56 out of 56), the middle third colour descriptors had a sorting reliability of 88%
O 94/09920 21 ~ P ~ /GB93/02151 (49 out of 56) and the tail third shape descriptors had a sorting reliability of 98% (55 out of 56). When these four sets of results were combined (as in the previous section) a sorting reliability of 98% (55 out of 56) was obtained. The one fish which was not classified correctly was a sprat and it was classified as 50%
probability of being sprat and 50% herring.
'Multivariate Analysis of Ecological Communities'. Ch~ ~n and HP11, London, F.ngl Pnd ( 1987).
Since there were n species of fish, every new fish to be sorted for species was given n scores, one from each classification function.
Fish were sorted as the species whose function had the highest score.
For the discriminant analysis the fish have to be split into two sets (the calibration and the test set). The species of the calibration set fish along with the variables is entered into the processor and from these the discriminant analysis algorithm produces the Fisher classification coefficients. The variables of the test set of fish are then introduced to the processor and this determines the species of these fish using the Fisher linear discriminant functions.
The arrangement of the processor with respect to the R, G, B input from the video camera and its associated equipment is shown in Figure 8. The R, G, B analog inputs from the camera are fed to respective 8 bit analog to digital converters connected in turn to look up tables and 2 megabytes of FIF0 store. This store is accessed by a 64 bit data bus further connected to a video random access memory and a video monitor, an 8 megabyte DRAM and the central processing unit (CPU).
The CPU has outputs which control the deflectors of the divertor col1veyor.
In experiments carried out using apparatus configured as described in Example l and in Figures 5 to 7 there were at most 35 fish of each species, hence where possible 20 fish of each species are used as the calibration set. This is because the larger the calibration set the ~~~O 94/09920 ~ 1 ~ 7 S 0 2 P ~ /GB93/02151 better the discriminant analysis performs.
The aforesaid apparatus, with the processor pro l- cd to carry out descriminant analyis as described was provided with fish of either Demersal (Example 2) or Pelagic (Example 3) types, these not usually requiring sorting together.
EXAMPLE 2: A~aratus for sorting Desmersal fish.
The apparatus was set up such that the computer processor first sorted images according to aspect ratio As and then according to their area ratio Ar (see Table l in introduction ). This identified angler fish, skate, ling and hake. There remained two groups of fish which required further sorting. Group l consisted mainly of flat fish and some round fish which have large aspect ratios. Group 2 consisted exclusively of round fish. 18 fish species were sorted in total.
For Group l the simple shape grid was generated for each of the species of fish from which shape and colour descriptors were generated (example position of colour varying area shown in Fig 3) and these were subjected to discriminant analysis. For the calibration set the discriminant analysis sorted all of the fish (135 fish) correctly ie.
lO0~ sorting reliability for the shape descriptors and the 3 sets of colour descriptors. For the test set of fish the shape descriptors had a sorting reliability of 94% (92 out of 98), the front third colour descriptors had a sorting reliability of 95% (93 out ot 98), the middle third colour descriptors had a sorting reliability of 95%
(93 out of 98) and the tail third colour descriptors had a sorting reliability of 94% (92 out of 98). These four sets of results were combined by simply classifying a fish as the one most preferred by - adding the results together. In doing this the sorting reliability obtained was 100% (98 out of 98).
For the Group 2 fish the shape grid was used. From this the shape and colour descriptors were obtained (examples of colour varying areas shown in Fig 4) and these were then subjected to discriminant analysis. For the calibration set the discriminant analysis sorted all of the fish (117 fish) correctly, ie. a 100% sorting reliability for the shape descriptors and the 3 sets of colour descriptors. For the test set the shape descriptors had a sorting reliability of 90%
(69 out of 77), the front third colour descriptors had a sorting reliability of 96% (74 out of 77), the middle third colour descriptors had a sorting reliability of 100% (77 out of 77) and the tail third colour descriptors had a sorting reliability of 100% (77 out of 77).
When these four sets of results were combined as before a sorting reliability of 100% (77 out of 77) was obtained.
Hence the 18 species of demersal fish used in these experiments were sorted with a reliability of 100% (133/133 test fish).
~xamvle ~: Ap~aratus for sorting Pel~gic fish.
The five main species of pelagic fish have similar values of Ar and As therefore the procedure of Table 3 does not work on pelagic species.
Shape grid were generated for all of the pelagic fish and from these the shape and colour descriptors were calculated (see examples of position of colour varying areas Flg 5) and subjected to discriminant analysis. For the calibration set the discriminant analysis sorted all of the fish (80 fish) correctly ie a 100% sorting reliability for the shape descriptors and the 3 (front, middle and tail) sets of colour descriptors. For the test set of fish the shape descriptors had a sorting reliability of 98% (55 out of 56), the front third colour descriptors had a sorting reliability of 100% (56 out of 56), the middle third colour descriptors had a sorting reliability of 88%
O 94/09920 21 ~ P ~ /GB93/02151 (49 out of 56) and the tail third shape descriptors had a sorting reliability of 98% (55 out of 56). When these four sets of results were combined (as in the previous section) a sorting reliability of 98% (55 out of 56) was obtained. The one fish which was not classified correctly was a sprat and it was classified as 50%
probability of being sprat and 50% herring.
Claims (29)
1. An apparatus for indicating the type of a fish comprising a means for receiving light from the fish and generating an image of it in the form of a set of values therefrom, a means for storing the set of values in a computer memory, a means for assigning the values to areas of the image of the fish, and a means for using these to determine and indicate the type of the fish, characterised in that the apparatus comprises a means for determining values indicative of the colour of all or some of the areas of the fish and that the type of fish is indicated by a means which compares the values indicative of colour by area of the fish with predetermined such values stored in the memory or a processor associated with it, these being characteristic of a particular type of fish, and a means which designates the fish as that type having stored values to which the determined values correspond.
2. An apparatus as claimed in claim 1 wherein the indication is used to control a sorting mechanism for directing the fish to a predetermined reception area associated with the type of fish with which it is designated to most closely correspond.
3. An apparatus as claimed in claim 1 or claim 2 wherein the orientation of the image of the fish is determined with respect to which end is the front end of the fish and which is the back end and/or which is the upper surface and which is the lower surface, and the colour and shape descriptors are related to these.
4. An apparatus for indicating the type of a fish comprising (a) a means for storing one or more sets of values indicative of predetermined length, widths and areas, and the distribution of colour in these areas, each set characteristic of an image of a particular sort of fish;
(b) means for receiving the image of a fish, the type of which is to be indicated, in a camera;
(c) means for receiving the image from the camera in the form of a set of values and storing these in a computer memory means;
(d) means for determining boundary values of the image of the fish;
(e) means for determining values representing predetermined lengths, widths and/or areas of the image and their ratios, (f) means for determining values indicative of colour of predetermined areas of the image, (g) means for determining which end of the image is representative of the front end and which is representative of the back end of the fish and/or means for determining which is the upper and which is the lower surface, and comparing the oriented values from (d) (e) and (f) with corresponding values stored in the means (a) as characteristic of a particular sort of fish;
(h) indicating means which indicates the type of fish to be that having stored values corresponding with the determined values.
(b) means for receiving the image of a fish, the type of which is to be indicated, in a camera;
(c) means for receiving the image from the camera in the form of a set of values and storing these in a computer memory means;
(d) means for determining boundary values of the image of the fish;
(e) means for determining values representing predetermined lengths, widths and/or areas of the image and their ratios, (f) means for determining values indicative of colour of predetermined areas of the image, (g) means for determining which end of the image is representative of the front end and which is representative of the back end of the fish and/or means for determining which is the upper and which is the lower surface, and comparing the oriented values from (d) (e) and (f) with corresponding values stored in the means (a) as characteristic of a particular sort of fish;
(h) indicating means which indicates the type of fish to be that having stored values corresponding with the determined values.
5. An apparatus as claimed in claim 4 wherein the indication means controls a mechanisms which directs the fish to a predetermined sorting reception area associated with the type of fish.
6. A sorting apparatus as claimed in any one of claims 1 to 4 wherein the fish are presented to a camera and their ratios of predetermined length, widths and/or areas, and the values indicative of colour in all or some of the areas, as derived from the image produced by that camera, are used to classify the fish, thus generating a control signal which directs the fish to an appropriate destination.
7. An apparatus comprising a conveyor belt for carrying one or more rows of fish, a means for illuminating the fish, a colour video camera, a video memory having a number of storage areas connected to the video camera, a means for storing the camera derived image values of a fish from the one or more rows in a first storage area of the memory, a means for determining the image size and orientation from stored edge and/or colour values, a means for using the stored edge values to generate shape descriptors the values of which are stored and compared with those already in the memory or logic of a computer processor associated therewith, and a means for determining the colour and/or light intensity of predetermined areas within the image periphery and storing and comparing values therefor with those stored in the memory or logic.
8. An apparatus as claimed in any one of the preceding claims comprising a colour video camera with red, green and blue signal outputs connected to a computer memory whereby a pattern of pixels corresponding to the image to be analysed can be produced, a processor for generating the finite elements of aspect and area ratios and the average red, green and blue values of selected parts of the image, the processor being connected to the memory, a comparator for comparing these elements with values previously generated from calibration of the apparatus using a number of fish of known type and stored in the memory or incorporating in the logic of a processor associated therewith, and a means for generating a score indicative of the number whether or not the fish is of a given type.
9. An apparatus as claimed in any one of the preceding claims wherein the image of the fish is recorded by the camera under both backlit and frontlit lighting conditions.
10. An apparatus as claimed in any one of the preceding claims wherein signals from the red, green and blue (RGB) outputs from the video camera are digitised to eight bits per colour using a frame grabbing board and then stored on a computer workstation where they are processed using algorithms.
11. An apparatus as claimed in any one of the preceding claims wherein the orientation of the fish with respect to which end is the head and which end is the tail is carried out by determining the width of the image at each of two points, each placed between a respective end and the mid-length of the image, and designating the larger width to be indicative of the head.
12. An apparatus as claimed in claim 11 wherein the two points are situated at a distance approximately one tenth of the total image length from each end.
13. An apparatus as claimed in any one of the preceding claims wherein the orientation of the fish with respect to which surface is the upper surface and which surface the lower surface is carried out by designating the lighter or less coloured surface the lower surface.
14. An apparatus as claimed in any one of the preceding claims wherein the image values are used to generate aspect ratio (As) and area ratio (Ar) shape descriptors.
15. An apparatus as claimed in claim 14 wherein those images having aspect ratios of over 0.40 are indicated as those of flatfish and those below 0.40 as roundfish.
16. An apparatus as claimed in claim 15 wherein the image of the fish has an aspect ratio of over 0.40 and is used to generate a grid constructed using vertical lines from the central line of symmetry.
17. An apparatus as claimed in claim 14 wherein the image of the fish has an aspect ratio less than 0.40 and is used to generate a grid of quadrilateral elements constructed from lines normal to its central line of symmetry.
18. An apparatus as claimed in either of claims 16 or 17 wherein the grid has a number of width values generated from the fishes nose to its tail.
19. An apparatus as claimed in any one of the preceding claims wherein the average R, G, B values of the image are determined for the tail, the nose and for some or all of the elements of the shape grid, having a computer processor which carries out multivariate analysis processing using these values as descriptor values for determining the species of the fish.
20. An apparatus as claimed in claim 19 wherein the width values are used as descriptor values for multivariate analysis processing wherein they are used to determine the species of the fish.
21. An apparatus as claimed in claim 19 or 20 wherein the linear combination of the variables that best distinguishes between the different species of fish to be sorted is installed in the logic of the processor that carries out the multivariate analysis.
22. An apparatus according to any one of claims 19 to 21 wherein the multivariate analysis is discriminant analysis.
23. An apparatus as claimed in any one of claims 19 to 22 wherein discriminant functions are generated by introducing one variable at a time wherein if the new function satisfies discriminant analysis criterion then it is accepted and if not, it is rejected along with the last variable introduced.
24. An apparatus as claimed in claim 23 wherein the classification score for species group i, is given by a linear combination of Cij's:
jmax Ci = .SIGMA. Cij Q(j) + Cio (6) j=1 where i = 1,2 ... n (n = no of species of fish), Cio is a constant and the Q(j)'s are the raw values of the variables.
24. An apparatus as claimed in claim 23 wherein the classification score for species group i, is given by a linear combination of Cij's:
jmax Ci = .SIGMA. Cij Q(j) + Cio (6) j=1 where i = 1,2 ... n (n = no of species of fish), Cio is a constant and the Q(j)'s are the raw values of the variables.
24. An apparatus as claimed in any one of claims 21 to 23 wherein the variables used comprise width shape descriptors, a length descriptor, the R, G, B average values of the nose and front third shape grid elements of the fish, the R, G, B average values of the middle third shape grid elements of the fish, and the R, G, B average values of the tail and tail third shape grid elements of the fish.
25. An apparatus as claimed in any one of claims 21 to 24 wherein fish to be sorted for species are given n scores, one from each classification function, and are sorted as the species whose function had the highest score.
26. An apparatus as claimed in claim 25 adapted such that when the species of a calibration set of fish inputted to the processor along with the variables, the discriminant analysis algorithm produces the Fisher classification coefficients, such that when the variables of the test set of fish are introduced to the processor it can determine the species of these fish using the Fisher linear discriminant functions.
27. An apparatus as claimed in any one of the preceding claims suitable for indicating type of and/or sorting desmersal fish characterised in that it first sorts the fish images according to aspect ratio (As), then according to area ratio (Ar), as defined herein, then subject to multivariate analysis.
28. An apparatus as claimed in any one of claims 1 to 27 substantially as described in any one of the Examples 1, 2 or 3.
29. A method for operating an apparatus as claimed in any one of the claims 1 to 27 substantially as described herein in any one of Examples 1, 2 or 3.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| GB929222338A GB9222338D0 (en) | 1992-10-23 | 1992-10-23 | Fish sorting machine |
| GB9222338.7 | 1992-10-23 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2147602A1 true CA2147602A1 (en) | 1994-05-11 |
Family
ID=10723974
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA002147602A Abandoned CA2147602A1 (en) | 1992-10-23 | 1993-10-19 | Fish sorting machine |
Country Status (4)
| Country | Link |
|---|---|
| EP (1) | EP0665774A1 (en) |
| CA (1) | CA2147602A1 (en) |
| GB (1) | GB9222338D0 (en) |
| WO (1) | WO1994009920A1 (en) |
Families Citing this family (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB9719522D0 (en) * | 1997-09-12 | 1997-11-19 | Newman Paul B D | Automated packaging |
| FR2809642B1 (en) * | 2000-06-06 | 2004-06-04 | Univ La Rochelle | METHOD FOR SORTING OBJECTS BY VIDEOMETRY AND INSTALLATION FOR IMPLEMENTING SAME |
| FR2860581B1 (en) * | 2003-10-03 | 2006-04-14 | A Resposabilte Limitee De La B | PROCESS AND INSTALLATION FOR QUALITY CONTROL IN BAKERY |
| AT502180B1 (en) | 2003-06-17 | 2007-02-15 | Unger Roland Ing | DEVICE FOR DETECTING UNIDENTIFIED OBJECTS |
| NO325882B1 (en) * | 2006-11-06 | 2008-08-11 | Catching Technology As | Method and system for automatic classification of fish underwater. |
| WO2009015486A1 (en) * | 2007-08-02 | 2009-02-05 | Montrose Technologies Inc. | Apparatus for inspecting and grouping articles traveling on a conveyor |
| ES2338293B1 (en) * | 2007-12-20 | 2011-03-10 | Instituto Valenciano De Investigaciones Agrarias | MACHINE FOR CLASSIFICATION OF GAJOS DE MANDARINA. |
| ES2352713B1 (en) * | 2008-04-24 | 2011-10-13 | Instituto Valenciano De Investigaciones Agrarias | MACHINE FOR INSPECTION AND AUTOMATIC SELECTION OF ARILOS DE GRANADA BY ARTIFICIAL VISION. |
| EP2440064A1 (en) | 2009-06-08 | 2012-04-18 | Marel Iceland EHF | A system and method for sorting of items prior to processing |
| NO331843B1 (en) * | 2010-06-24 | 2012-04-16 | Maskon As | Method and system for vaccination and sorting of fish |
| BE1020295A3 (en) * | 2011-11-16 | 2013-07-02 | Acro Khlim | METHOD FOR SORTING OBJECTS AND SORTING DEVICES THEREFOR |
| CN104624515A (en) * | 2013-11-08 | 2015-05-20 | 华中农业大学 | Machine vision based freshwater fish variety on-line classification system |
| CN103621454A (en) * | 2013-11-27 | 2014-03-12 | 蔡德所 | Fish body length lossless measurement water tank |
| ES2478420B1 (en) * | 2014-04-09 | 2015-06-02 | Tecnología Marina Ximo, S.L. | Automated system and procedure for classification of frozen tunas by species |
| ES2552397B1 (en) * | 2014-05-27 | 2016-09-14 | Tecnología Marina Ximo, S.L. | System and method for estimating tuna caught by species on board fishing vessels |
| NO336911B1 (en) * | 2014-12-11 | 2015-11-23 | Melbu Systems As | Method for estimating the weight of fish |
| ES2952522T3 (en) | 2015-02-26 | 2023-11-02 | Lambhusasund Ehf | Apparatus for forming images of, classifying and grouping whole fish |
| ES2649056B1 (en) * | 2016-07-07 | 2018-09-12 | Universidade Da Coruña | Detection and measurement system of fish and moving objects underwater |
| AU2018313729B2 (en) * | 2017-08-07 | 2021-08-05 | Pharmaq As | Live fish processing system, and associated method |
| CN107931148A (en) * | 2017-12-26 | 2018-04-20 | 安徽工程大学 | A kind of live body crab class intelligence separation system and method |
| CN108566925A (en) * | 2018-06-22 | 2018-09-25 | 中国水产科学研究院渔业机械仪器研究所 | Fishing net pumping cleaning classification integration apparatus and its working method |
| CN113145483B (en) * | 2021-02-24 | 2023-06-23 | 陕西蒲城好邦食品有限公司 | Meat piece blood stain monitoring devices is used in chicken production |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA1251863A (en) * | 1988-02-29 | 1989-03-28 | Kevin Mccarthy | Fish sorting machine |
| US5085325A (en) * | 1988-03-08 | 1992-02-04 | Simco/Ramic Corporation | Color sorting system and method |
| IS1666B (en) * | 1991-02-19 | 1997-11-14 | Marel Hf | Method and apparatus for determining the volume, shape and weight of fish or other parts |
-
1992
- 1992-10-23 GB GB929222338A patent/GB9222338D0/en active Pending
-
1993
- 1993-10-19 EP EP93923018A patent/EP0665774A1/en not_active Withdrawn
- 1993-10-19 WO PCT/GB1993/002151 patent/WO1994009920A1/en not_active Application Discontinuation
- 1993-10-19 CA CA002147602A patent/CA2147602A1/en not_active Abandoned
Also Published As
| Publication number | Publication date |
|---|---|
| GB9222338D0 (en) | 1992-12-09 |
| WO1994009920A1 (en) | 1994-05-11 |
| EP0665774A1 (en) | 1995-08-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CA2147602A1 (en) | Fish sorting machine | |
| Strachan | Recognition of fish species by colour and shape | |
| US4963035A (en) | Fish sorting machine | |
| US5072127A (en) | Engineered video inspecting lighting array | |
| Vibhute et al. | Applications of image processing in agriculture: a survey | |
| Laykin et al. | Image–processing algorithms for tomato classification | |
| US5443164A (en) | Plastic container sorting system and method | |
| Shearer et al. | Color and defect sorting of bell peppers using machine vision | |
| EP0737112B1 (en) | Scrap sorting system | |
| CA1306976C (en) | Optical system and method for sorting objects according to shape | |
| Sites et al. | Computer vision to locate fruit on a tree | |
| Mohammadi Baneh et al. | Mechatronic components in apple sorting machines with computer vision | |
| EP0498811B1 (en) | Engineered video inspection lighting array | |
| JPS5973089A (en) | Electronic flaw scanner | |
| US6043445A (en) | Apparatus for color-based sorting of titanium fragments | |
| Deck et al. | Comparison of a neural network and traditional classifier for machine vision inspection of potatoes | |
| US5184732A (en) | Shape sorting | |
| Strachan | Sea trials of a computer vision based fish species sorting and size grading machine | |
| CN112287748A (en) | Mobile article classification system and method based on self-learning technology | |
| Chopde et al. | Developments in computer vision system, focusing on its applications in quality inspection of fruits and vegetables-A review | |
| Park et al. | Intensified multispectral imaging system for poultry carcass inspection | |
| Alchanatis et al. | Classification of tissue culture segments by colour machine vision | |
| EP0342354A2 (en) | Color sorting apparatus | |
| Recce et al. | Video grading of oranges in real-time | |
| Khriji et al. | Artificial intelligent techniques for palm date varieties classification |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FZDE | Discontinued |