Rotation-invariant texture analysis using Radon and Fourier transforms

Texture analysis is a basic issue in image processing and computer vision, and how to attain the Rotation-invariant texture characterization is a key problem. This paper proposes a rotation-invariant texture analysis technique using Radon and Fourier transform. This method uses Radon transform to convert rotation to translation, then utilizes the Fourier transform and takes the modules of the Fourier transform of these functions to make the translation invariant. A k-nearest-neighbor rule is employed to classify textures images. The proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the Radon transform step. To test and evaluate the method, several different kinds of experiments are employed. Experiments results show the feasibility of the proposed method and its robustness to additive white noise.