Determination of Lamb Grades using Texture Analysis and Neural Networks

M.R. Chandraratne, S. Samarasinghe, D. Kulasiri, C. Frampton, and R. Bickerstaffe (New Zea


Image analysis, Artificial neural networks, Co-occurrencetexture features, Discriminant analysis, Computer vision,Lamb grading


This paper presents a lamb carcass classification system based on texture analysis and artificial neural networks (ANNs). Digital images of lamb chops were used to calculate twelve image geometric variables. In addition, a set of ninety texture features was used to extract the textural information. Texture analysis is based on the co occurrence matrix method. Principal component analysis (PCA) was used to reduce the dimensionality of feature spaces. The reduced feature space, with six geometric variables and eight co occurrence texture variables, was used for the analysis. Three-, four- and five-layer multilayer perceptron (MLP) networks and Discriminant function analysis (DFA) were performed on the data. The classification accuracy from three-layer MLP (93.1%) was 14% better than that from DFA. This study shows the potential of neural networks combined with texture analysis for lamb grading.

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