Affine - Invariant Texture Classification
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Affine Invariant Texture Analysis Based on Structural Properties
This paper presents a new texture analysis method based on structural properties. The texture features extracted using this algorithm are invariant to affine transform (including rotation, translation, scaling, and skewing). Affine invariant structural properties are derived based on texel areas. An area-ratio map utilizing these properties is introduced to characterize texture images. Histogra...
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In this paper, we develop a new approach for texture classification independent of affine transforms. Based on spectral representation of texture images under affine transform, anisotropic scale invariant signatures of orientation spectrum distribution are extracted. Peaks distribution vector (PDV) obtained on the distribution of these signatures captures texture properties invariant to affine ...
متن کاملAffine-Gradient Based Local Binary Pattern Descriptor for Texture Classification
We present a novel Affine-Gradient based Local Binary Pattern (AGLBP) descriptor for texture classification. It is very hard to describe complicated texture using single type information, such as Local Binary Pattern (LBP), which just utilizes the sign information of the difference between pixel and its local neighbors. Our descriptor has three characteristics: 1) In order to make full use of t...
متن کاملA Sparse Texture Representation Using Affine-Invariant Regions
This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and nonrigid deformations. At the feature extraction stage, a sparse set of affine-invariant local patches is extracted from the image. This spatial selection process permits the computation of characteristic scale and neighborhoo...
متن کاملSparse Texture Representation Using Affine-Invariant Neighborhoods
This paper proposes a novel texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and non-rigid deformations. Unlike many existing feature extraction methods, which treat the neighborhood of every pixel as a candidate texture element, the proposed algorithm works by selecting a sparse set of affine-invarian...
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تاریخ انتشار 2000