A new approach for wet blue leather defect segmentation

Authors:
Patricio Villar;Marco Mora;Paulo Gonzalez
Affiliations:
Les Fous du Pixel Image Processing Research Group Department of Computer Science, Catholic University of Maule, Talca, Chile;Les Fous du Pixel Image Processing Research Group Department of Computer Science, Catholic University of Maule, Talca, Chile;Les Fous du Pixel Image Processing Research Group Department of Computer Science, Catholic University of Maule, Talca, Chile
Venue:
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2011

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Abstract

In the process plants where beef skin is processed, leather classification is done manually. An expert visually inspects the leather sheet and classifies them based on the different types of defects found on the surface, among other factors. In this study, an automatic method for defect classification of the Wet Blue leather is proposed. A considerable number of descriptors are computerized from the Gray Scale image and the RGB and HSV color model. Features were chosen based on the Sequential Forward Selection method, which allows a high reduction of the numbers of descriptors. Finally, the classification is implemented by using a Supervised Neural Network. The problem formulation is adequate, allowing a high rate of success, obtaining a method with wide range of possibilities for implementation.