Resumen

RAMIREZ ALONSO, Graciela María de Jesús  y  CHACON MURGUIA, Mario Ignacio. Wood Defects Classification Using Artificial Neural Network. Comp. y Sist. [online]. 2005, vol.9, n.1, pp.17-27. ISSN 2007-9737.

This paper describes a neural classifier to classify 7 different wood defects called knots. Human visual inspection of these defects involves a high degree of complexity due to inter-class variance. 2D Gabor filters were used for feature extraction. These filters are selective band pass filters to orientation and frequency. These filters are used where texture is an important feature. The method of principal component analysis was used to reduce the number of features generated by the Gabor filters. The neural network implemented was a multilyer perceptron with 3 layers trained with the Resalient backpropagation algorithm. The performance of the classifier was 83.91% of correct classification. This result is acceptable considering that the performance of a human inspector is 75% to 85%.

Palabras llave : Neural networks; Gabor filters; image processing.

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