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Fuzzy aura matrices for texture classification

Abstract : The aura concept has been developed from the set theory and is an efficient tool to characterize texture images. It is based on the notion of “aura set” and on the associated “aura measure” that involve the neighborhood of each image pixel. In this paper, we propose to extend this concept to the framework of fuzzy sets in order to take the imprecise nature of images into account. We define the notions of fuzzy aura sets and of aura measures to compute fuzzy aura matrices as texture descriptors. Fuzzy aura measures assume no restrictions about the neighborhood shape, size, and spatial invariance. Extensive tests of texture classification on Outex benchmark datasets show that fuzzy aura matrices computed with spatially-variant neighborhoods oft en outperform other power ful texture descriptors on both gray-level and color images
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Submitted on : Monday, January 22, 2018 - 10:00:57 AM
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Kamal Hammouche, Olivier Losson, Ludovic Macaire. Fuzzy aura matrices for texture classification. Pattern Recognition, Elsevier, 2016, 53, pp.212-228. ⟨10.1016/j.patcog.2015.12.001⟩. ⟨hal-01242418⟩

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