1994
DOI: 10.1117/12.177115
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Multichannel filtering for image texture segmentation

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Cited by 67 publications
(38 citation statements)
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“…Figure 2 shows one texture set containing three non-boundary textures and three boundary textures. In order to extract the RF response for the given textures, each texture was preprocessed by a Laplacian of Gaussian (LoG) filter, a popular choice for edge detection, followed by a transformation of the edge into detectable discontinuities [12]. The LoG filter is defined as below…”
Section: Methodsmentioning
confidence: 99%
“…Figure 2 shows one texture set containing three non-boundary textures and three boundary textures. In order to extract the RF response for the given textures, each texture was preprocessed by a Laplacian of Gaussian (LoG) filter, a popular choice for edge detection, followed by a transformation of the edge into detectable discontinuities [12]. The LoG filter is defined as below…”
Section: Methodsmentioning
confidence: 99%
“…Comparative studies performed by Randen et al [32,64] and Chen et al [33] indicate that the Gabor features in most of the cases outperform the other methods (ring=wedge ÿlter, spatial ÿlter, quadrature mirror ÿlter, eigenÿlter, wavelet transform) regarding the complexity and overall error rate mentioned in their literature. But the Gabor features su er from a number of di culties.…”
Section: Multichannel Gabor ÿLtermentioning
confidence: 99%
“…Many methods have been proposed to extract texture features, such as the co-occurrence matrices [1], the Markov random fields [2], fractals [3], and the Gabor filters [4], wavelet transforms [5,6] and quadrature mirror filters [7]. Recently, Randen and Husøy did an extensive review and comparative study for texture classification on most major filtering-based approaches [8].…”
Section: Introductionmentioning
confidence: 99%