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|Title: ||Lamb carcass classification system based on computer vision. Part 2, Texture features and neural networks|
|Author: ||Chandraratne, M. R.|
Frampton, Chris M.
|Date: ||Aug-2003 |
|Publisher: ||Lincoln University. Applied Computing, Mathematics and Statistics Group.|
|Series/Report no.: ||Research report (Lincoln University (Canterbury, N.Z.). Applied Computing, Mathematics and Statistics Group) ; 08/2003|
|Item Type: ||Monograph|
|Abstract: ||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 is based on image and texture analyses. Digital images of lamb chops were used to calculate twelve image geometric variables. In addition, a set of ninety textural features was used to extract the textural information from the acquired images. Texture analysis is based on the grey level co-occurrence matrix method.
Principal component analysis (PCA) was used to reduce the dimensionality of feature spaces. Two feature sets were generated. These feature sets comprised of 14
principal component (PC) scores calculated from the original variables and 14 variables
selected from the original set of variables. Both feature spaces were used for neural
network and discriminant analysis. Several network configurations were tested and the classification accuracy of 93% was achieved from three-layer multilayer perceptron (MLP) network. Its performance was 14% better than that from the Discriminant function analysis (DFA). The study shows the predictive potential of combining neural networks with texture analysis for lamb grading.|
|Persistent URL (URI): ||http://hdl.handle.net/10182/244|
|Appears in Collections:||Applied Computing Research Report series|
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