Wood Defects Classification Using Artificial Neural Network
Graciela María de Jesús Ramírez Alonso y Mario Ignacio Chacón Murguía
Instituto Tecnológico de Chihuahua Laboratorio de DSP y Visión Av. Tecnológico 2909, Chihuahua, Chih., México C.P. 31310 Tel.4–13–74–74 Ext 112 y 114 gramirez@itchihuahua.edu.mx ; mchacon@itchihuahua.edu.mx
Artículo recibido en junio 06, 2004
Aceptado en abril 01, 2005
Resumen
Este artículo describe un clasificador neural que diferencía entre 7 tipos de defectos en maderas llamados botones. La inspección visual de estos defectos por humanos tiene un alto grado de complejidad debido a la varianza intraclase. Las características utilizadas se extrajeron de las imágenes de maderas mediante filtros Gabor de 2D. Estos filtros son pasa banda selectivos a la orientación y frecuencia, muy utilizados para imágenes en donde la textura es un factor importante. Para optimizar las características se realizó una reducción de dimensión del resultado de los filtros Gabor mediante el método de Análisis de Componentes Principales. La red neural que se implementó fue una red Perceptrón multicapa de 3 capas entrenada con el algoritmo de Resalient Backpropagation. La tasa de reconocimiento de la red fue de un 83.91%, siendo este resultado aceptable teniendo en cuenta que un inspector humano alcanza un reconocimiento entre el 75 y 85%.
Palabras Clave: Redes neurales, filtros Gabor, procesamiento de imágenes.
Abstract
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%.
Keywords: Neural networks, Gabor filters, image processing.
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]]>Agradecimientos
Los autores agradecen a CONACYT por el financiamiento de este trabajo bajo el convenio PFPN–03–29–05.
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