EP1196761A1 - Analyse des donnees d'images d'objets - Google Patents

Analyse des donnees d'images d'objets

Info

Publication number
EP1196761A1
EP1196761A1 EP00941800A EP00941800A EP1196761A1 EP 1196761 A1 EP1196761 A1 EP 1196761A1 EP 00941800 A EP00941800 A EP 00941800A EP 00941800 A EP00941800 A EP 00941800A EP 1196761 A1 EP1196761 A1 EP 1196761A1
Authority
EP
European Patent Office
Prior art keywords
carcase
meat
colour
yield
data
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.)
Withdrawn
Application number
EP00941800A
Other languages
German (de)
English (en)
Inventor
Kurt Malmstrom
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
RMS Research Management Systems Inc
Original Assignee
RMS Research Management Systems Inc
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Priority claimed from AUPQ1544A external-priority patent/AUPQ154499A0/en
Priority claimed from AUPQ2828A external-priority patent/AUPQ282899A0/en
Application filed by RMS Research Management Systems Inc filed Critical RMS Research Management Systems Inc
Publication of EP1196761A1 publication Critical patent/EP1196761A1/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/02—Food
    • G01N33/12—Meat; Fish

Definitions

  • This invention relates to image data analysis for objects such as meat carcases and meat cuts although the invention may also be applicable to other agricultural, mineral or
  • colour data captured by a colour video camera has been utilised in the form of R, G, and B values (red, green and blue values) in yield equations derived from multiple field runs as described above. Particular care ought to be taken to ensure as far as
  • a method of analysing colour image data relating to a target object to derive or predict a property of the object of which colour is an indicator including the step of processing the colour data to derive ight intensity independent measures of colour values, followed by the step of calculating the property of the object utilising the light intensity independent colour measures in a predictive
  • the light intensity independent colour measures are variables and the property of the object is calculated from solving the predictive equation.
  • the light intensity independent measures of colour values are used in equations developed to predict a quantitative meat or carcase quality measure, e.g. "yield” in Australia, "conformation” or “fat score” in the
  • the method includes the step of processing the colour data for a carcase to derive light intensity independent measures of colour values for the carcase, followed by the step of calculating the meat yield of the carcase utilising the light intensity independent colour measures in a yield predictive equation.
  • the intensity normalised class CRiGil has been adapted from the prior CRGB class consisting of Red, Green and Blue values.
  • the class consists of the member variables Ri; Gi;
  • a digitiser offset is preferably subtracted (since the offset associated with a digitiser for digitising measured RGB values in a colour data capture system is obviously not affected by light
  • R, R + G + B - 3k
  • yield equations relating measured or calculated parameters of the carcases could be derived 5 by multiple regression analysis (or other statistical analysis techniques) to best fit the data and optimise the fitting or prediction of the actual saleable meat yield.
  • R ! is the intensity based red value for the same "area 1 "
  • G is the intensity based green value for the same "area 1 "
  • d is the distance from the tail to the hind leg bottom, when projected onto a
  • d 2 is the distance from the brisket to the tail.
  • f is the ratio w/L, where w is the distance from the point where the hook suspending
  • the beef carcass passes through the hind leg to the point at the end of the profile of the butt, when projected onto the longitudinal line, and
  • L is the length of the carcass
  • f is the ratio x/L, where x is the distance from the hook to the point of the armpit, when projected onto the longitudinal line, and L is the length of the carcass,
  • S ! is a measure of the degree of "plumpness" of the shape of the butt, e.g. derived by obtaining a measure of the extent of departure of the butt profile from the line from the point of the tail to the bottom of the hind leg,
  • G ⁇ is the intensity normalised green value for a predetermined "area 2" of the carcass
  • R l3 is the intensity normalised red value for a different "area 3" of the carcass
  • R l4 is the intensity normalised red value for a different "area 4" of the carcass
  • B 3 is the intensity based red value for "area 3".
  • the derived equations will be different depending on the use of selected ones of the numerous variables including dimensional variables, ratios of dimensions, other measures such as the measure of the shape of the butt.
  • the sizes and locations of the predetermined areas of the carcase where colour measurements are taken and used in the predictive equations will very substantially affect the final derived constants in the equations.
  • tissue colours of a real beef carcase was measured multiple times on a number of days during the period of the yield trials. Over a trial when the fake carcase was presented 37 times over a number of days, yield equations (1) and (3) exhibited only very small changes in the predicted yield - minimum -0.062% and maximum +0.102% deviation from a median predicted yield. On the other hand, yield equations (2) and (4) displayed a drift of minimum -0.39% and maximum +0.16% from the median.
  • intensity normalised yield equations (1) and (3) showed only very small change in predicted yield and small change in the RMS of the changes for this trial with the failed light bulb, whereas the intensity dependent yield predictive equations (2) and (4) showed very large changes in predicted yield and RMS value for this trial with the failed light bulb. By chance, this demonstrated that the use of intensity normalised equations are robust to such changes in illumination conditions.
  • each carcase was categorised into one of the six predetermined categories and the data
  • the BCS produces not only its prediction of saleable meat yield, but also a yield that
  • This component yield (CompWy) was added to a weighted CAS predicted yield representing the currently selected carcase type.
  • CAS refers to a "Chiller Assessment System” (available from Viascan Quality Assessment, of Beenleigh, Queensland, Australia) which provides measures relating to meat yield after further analyses later in the processing operation in a chiller. This weighted addition must be applied off-line at the end of the processing operations in the abattoir.
  • the formula that is implemented was
  • k' is defined according to which carcase type has been selected. The appropriate values are shown below in the table. Note that the CAS yield equations exist for only three carcase categories. These are: bull, cow, and table beef (where the "table beef category includes all beef from the four subcategories of the BCS).
  • yield is the primary measure used for carcase grading in Australia where the invention has been developed. However, in other countries or regions, there can be different parameters used to grade meat such as meat carcases.
  • EUROP scoring or grading system
  • the present invention is equally applicable to the process of calculating the conformation and fat scores in the EUROP system for a meat portion or carcase using light intensity independent colour measures in appropriate predictive equations. It will be appreciated that the capture of colour data for a carcase (together with other data such as dimensional data), can be used in an

Landscapes

  • Chemical & Material Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biochemistry (AREA)
  • Food Science & Technology (AREA)
  • Analytical Chemistry (AREA)
  • Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Medicinal Chemistry (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Spectrometry And Color Measurement (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)
  • Image Analysis (AREA)

Abstract

L'invention se rapporte à un procédé d'analyse des données d'images couleur associées à un objet cible, et notamment à une carcasse d'animal, dans le but de dériver ou de prédire une propriété de cet objet dont la couleur est un indicateur, telle que le rendement en viande. Ledit procédé consiste à traiter les données de couleur de manière à dériver des mesures indépendantes de l'intensité lumineuse ou 'd'intensité normalisée' des valeurs de couleurs, puis à calculer la propriété de l'objet au moyen de ces mesures de couleurs indépendantes de l'intensité lumineuse dans une équation prédictive dans laquelle ces mesures sont des variables et à résoudre cette équation prédictive de manière à obtenir la propriété de l'objet. L'équation prédictive peut être développée à partir de données recueillies au cours d'une expérience de collecte de données utilisant les images saisies pour des objets cible réels. On corrèle ensuite les mesures de couleurs indépendantes de l'intensité lumineuse obtenues à partir de ces données avec la propriété mesurée réelle de chacun des vrais objets cibles de manière à dériver l'équation prédictive par des techniques d'analyse statistique permettant d'adapter au mieux les données. Dans le cas de l'analyse de carcasses d'animaux, la propriété prédite peut être le 'rendement', la 'conformation' ou la 'teneur en graisses' d'une carcasse dans un système normalisé de classification de carcasses ou peut être la 'classe de rendement ' ou 'la classe de qualité' de la viande d'une carcasse dans un système normalisé de classification de la viande.
EP00941800A 1999-07-09 2000-07-10 Analyse des donnees d'images d'objets Withdrawn EP1196761A1 (fr)

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
AUPQ154499 1999-07-09
AUPQ1544A AUPQ154499A0 (en) 1999-07-09 1999-07-09 Image data analysis
AUPQ282899 1999-09-14
AUPQ2828A AUPQ282899A0 (en) 1999-09-14 1999-09-14 Image data analysis
PCT/AU2000/000830 WO2001004607A1 (fr) 1999-07-09 2000-07-10 Analyse des donnees d'images d'objets

Publications (1)

Publication Number Publication Date
EP1196761A1 true EP1196761A1 (fr) 2002-04-17

Family

ID=25646096

Family Applications (1)

Application Number Title Priority Date Filing Date
EP00941800A Withdrawn EP1196761A1 (fr) 1999-07-09 2000-07-10 Analyse des donnees d'images d'objets

Country Status (8)

Country Link
EP (1) EP1196761A1 (fr)
AR (1) AR024689A1 (fr)
BR (1) BR0012349A (fr)
CA (1) CA2378741A1 (fr)
MX (1) MXPA02000286A (fr)
NZ (1) NZ516814A (fr)
UY (1) UY26237A1 (fr)
WO (1) WO2001004607A1 (fr)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003034059A1 (fr) * 2001-10-18 2003-04-24 Machinery Developments Limited Appareil et procede d'analyse de morceaux de viande
CN100376888C (zh) * 2004-11-02 2008-03-26 江苏大学 牛肉胴体质量的计算机视觉检测分级方法及装置
WO2006086450A1 (fr) 2005-02-08 2006-08-17 Cargill Incorporated Tri de viande
JP7125802B1 (ja) 2021-06-15 2022-08-25 有限会社 ワーコム農業研究所 牛肉品質判定装置

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
NO862509L (no) * 1985-09-04 1987-03-05 Westinghouse Canada Inc Anordning for maaling av fargetonen hos en kjoettproeve.
DK163382C (da) * 1990-02-28 1992-08-03 Slagteriernes Forskningsinst Fremgangsmaade til bestemmelse af individuelle koedemners kvalitetsegenskaber
DE69329820T2 (de) * 1992-04-13 2001-06-07 Australian Meat & Live Stock Fleisch-bildanalyse.
DE4408604C2 (de) * 1994-03-08 1996-05-02 Horst Dipl Ing Eger Verfahren zur Bewertung von Schlachttierkörpern

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See references of WO0104607A1 *

Also Published As

Publication number Publication date
BR0012349A (pt) 2002-06-11
AR024689A1 (es) 2002-10-23
MXPA02000286A (es) 2004-09-30
CA2378741A1 (fr) 2001-01-18
UY26237A1 (es) 2000-10-31
NZ516814A (en) 2002-07-26
WO2001004607A1 (fr) 2001-01-18

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