A New Approach for Wet Blue Leather Defect Segmentation

  • Patricio Villar
  • Marco Mora
  • Paulo Gonzalez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)


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.


Local Binary Pattern Gray Scale Image Sequential Forward Selection Leather Processing Supervise Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Patricio Villar
    • 1
  • Marco Mora
    • 1
  • Paulo Gonzalez
    • 1
  1. 1.Les Fous du Pixel Image Processing Research Group Department of Computer ScienceCatholic University of MauleTalcaChile

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