Oriented texture classification based on self-organizing neural network and Hough transform
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Data
1997-01-01
Autores
Marana, Aparecido Nilceu [UNESP]
da, L. [UNESP]
Velastin, S. A. [UNESP]
Lotufo, R. A. [UNESP]
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Resumo
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.
Descrição
Palavras-chave
Mathematical transformations, Neural networks, Hough transform, Kohonen's self organizing map, Oriented texture classification, Feature extraction
Como citar
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, v. 4, p. 2773-2775.