Wood Defects Classification Using a SOM/FFP Approach with Minimum Dimension Feature Vector

  • Mario I. Chacon
  • Graciela Ramirez Alonso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


This paper describes the design and implementation of a wood defect classifier. The defects are four different types of knots found in wood surfaces. Classification is based on features obtained from Gabor filters and supervised and non supervised artificial neural networks are used as classifiers. A Self-organizing neural network and a fuzzy Self-organizing neural network were designed as classifiers. The fuzzy SONN shows a reduction on the training time and had a better performance. A final classifier, a feedforward perceptron using the weights of the fuzzy SONN as initial weights turn to be the best classifier with a performance of 97.22% in training and 91.17% in testing. The perceptron classifier surpasses a human inspector task which has a maximum performance of 85%.


Wood Surface Gabor Filter Gabor Feature Fuzzy Parameter Defect Sample 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mario I. Chacon
    • 1
  • Graciela Ramirez Alonso
    • 1
  1. 1.DSP & Vision Lab.Chihuahua Institute of TechnologyChihuahua, Chih.Mexico

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