We develop a new approach for texture classification independent of affine transforms. Based on a spectral representation of texture images under affine transform, anisotropic scale invariant signatures of the orientation spectrum distribution are extracted. A peaks distribution vector (PDV) obtained on the distribution of these signatures captures texture properties invariant to affine distortion. The PDV is used to measure the similarity between textures. Experimental results show the efficiency of the PDV for affine invariant texture classificatio
Many natural textures comprise structural patterns and show strong self-similarity. We use affine sy...
Textures within real images vary in brightness, contrast, scale and skew as imaging conditions chang...
This paper introduces a texture representation suitable for recognizing images of textured surfaces ...
We develop a new approach for texture classification independent of affine transforms. Based on a sp...
We develop a new approach for texture classification independent of affine transforms. Based on a sp...
In this paper, we propose a new method of extracting affine invariant texture signatures for content...
In this paper, we propose a new method of extracting affine invariant texture signatures for content...
In this paper, we propose a new method of extracting affine invariant texture signatures for content...
This paper presents a new texture analysis method based on structural properties. The texture featur...
This paper presents a new texture analysis method based on structural properties. The texture featur...
This paper presents a new texture analysis method based on structural properties. The texture featur...
In this paper, we present a theoretically and computationally simple but efficient approach for rota...
In this paper, we present a theoretically and computationally simple but efficient approach for rota...
In this paper, we present a theoretically and computationally simple but efficient approach for rota...
Many natural textures comprise structural patterns and show strong self-similarity. We use affine sy...
Many natural textures comprise structural patterns and show strong self-similarity. We use affine sy...
Textures within real images vary in brightness, contrast, scale and skew as imaging conditions chang...
This paper introduces a texture representation suitable for recognizing images of textured surfaces ...
We develop a new approach for texture classification independent of affine transforms. Based on a sp...
We develop a new approach for texture classification independent of affine transforms. Based on a sp...
In this paper, we propose a new method of extracting affine invariant texture signatures for content...
In this paper, we propose a new method of extracting affine invariant texture signatures for content...
In this paper, we propose a new method of extracting affine invariant texture signatures for content...
This paper presents a new texture analysis method based on structural properties. The texture featur...
This paper presents a new texture analysis method based on structural properties. The texture featur...
This paper presents a new texture analysis method based on structural properties. The texture featur...
In this paper, we present a theoretically and computationally simple but efficient approach for rota...
In this paper, we present a theoretically and computationally simple but efficient approach for rota...
In this paper, we present a theoretically and computationally simple but efficient approach for rota...
Many natural textures comprise structural patterns and show strong self-similarity. We use affine sy...
Many natural textures comprise structural patterns and show strong self-similarity. We use affine sy...
Textures within real images vary in brightness, contrast, scale and skew as imaging conditions chang...
This paper introduces a texture representation suitable for recognizing images of textured surfaces ...