Concentric circular sampling for texture analysis
Hamzah Arof, Farzin Deravi · 2002
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.