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Prediction of lamb tenderness using texture features
(Lincoln University. Applied Computing, Mathematics and Statistics Group, 2003-08)
Meat quality is a subject of growing interest. The meat industry, in response to consumer demand for products of consistent quality, is placing more and more emphasis on quality assurance issues. Tenderness is an important ...
Lamb carcass classification system based on computer vision. Part 1, Texture features and discriminant analysis
(Lincoln University. Applied Computing, Mathematics and Statistics Group., 2003-08)
This paper presents a lamb carcass classification system based on image and texture analyses together with multivariate statistical techniques (principal component analysis, cluster analysis and discriminant function ...
Lamb carcass classification system based on computer vision. Part 2, Texture features and neural networks
(Lincoln University. Applied Computing, Mathematics and Statistics Group, 2003-08)
In this study, the ability of neural network models for lamb carcass classification was
compared with a multivariate statistical technique with respect to the classification
accuracy. The lamb carcass classification system ...
Determination of fat content in retail ready meat samples using image analysis
(Lincoln University. Applied Computing, Mathematics and Statistics Group, 2003-08)
As a result of constantly growing consumer expectations for meat quality, the meat industry is placing more and more emphasis on quality assurance issues. Fat content in meat influences some important meat quality ...