Concentric circular sampling for texture analysis

This paper introduces new texture descriptors that perform well for rotated and unrotated texture images without the need to be trained with rotated samples. The algorithm uses a circular sampling of the image to produce rotation invariant features. By combining features derived from multiple concentric circles a higher performance in texture classification is achieved.

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