Filtering methods for texture discrimination

Abstract Filtering methods have recently raised increasing interests in texture analysis due to their simulation of human vision. The goal of this paper is to evaluate the performance of four filtering methods including Fourier transform, spatial filter, Gabor filter and wavelet transform for texture discrimination. Experimental results on both natural textures and synthesized Markov random field (MRF) textures indicate that the wavelet features achieve almost the same recognition rate with the Gabor features, which is higher than the other two methods, whereas the computation time shows that the wavelet features are preferred.

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