No abstract

An important economic alternative for the semi-arid region of Brazil is the goat and sheep farming. Besides milk and meat, goat/sheep skins are much appreciated in the manufacturing of fine artifacts (e.g. shoes, bags & purses, wallets, and jackets). However, due to the extensive mode of raising/breeding and the informality of slaughtering, sheep/goat farmers deliver to industry skin pieces with different types and levels of defects. Then, at the industry, specialized workers have to classify/discriminate the skin pieces according to their qualities. This handmade work is timeconsuming and extremely dependent on the experience of the employee in charge of the skin-quality discrimination. Even the same employee may produce different classifications if he/she is asked to reclassify the skin lot. Thus, in order to handle these problems, in this paper we report the first results of a computer vision based system aiming at classifying automatically the quality of goat/sheep skin pieces. For this purpose, we compare the performances of statistical and neural network classifiers using several feature extraction techniques, such as Column-Variance, Haar wavelet transform, Non-Negative Matrix Factorization (NMF) and Principal Component Analysis (PCA). The obtained results clearly indicate a better performance for the neural network classifier using the Haar wavelet transform and PCA as feature extractors.