Wood defects classification using a SOM/FFP approach with minimum dimension feature vector

Authors:
Mario I. Chacon;Graciela Ramirez Alonso
Affiliations:
DSP & Vision Lab., Chihuahua Institute of Technology, Chihuahua, Chih., Mexico;DSP & Vision Lab., Chihuahua Institute of Technology, Chihuahua, Chih., Mexico
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 3
Cited 1

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Abstract

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%.