Oriented texture classification based on self-organizing neural network and Hough transform

• Autor(a) principal: Marana, Aparecido Nilceu [UNESP]
• Data de Publicação: 1997
• Outros Autores: da, L. [UNESP], Velastin, S. A. [UNESP], Lotufo, R. A. [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/ICASSP.1997.595364; http://hdl.handle.net/11449/64992
• topic: Mathematical transformations; Neural networks; Hough transform; Kohonen's self organizing map; Oriented texture classification; Feature extraction

This paper presents a technique for oriented texture classification which is based on the Hough transform and Kohonen's neural network model. In this technique, oriented texture features are extracted from the Hough space by means of two distinct strategies. While the first operates on a non-uniformly sampled Hough space, the second concentrates on the peaks produced in the Hough space. The described technique gives good results for the classification of oriented textures, a common phenomenon in nature underlying an important class of images. Experimental results are presented to demonstrate the performance of the new technique in comparison, with an implemented technique based on Gabor filters.