Rotation invariant pattern recognition using ridgelets, wavelet cycle-spinning and Fourier features
In this paper, we propose a rotation-invariant descriptor for pattern recognition by using ridgelets, wavelet cycle-spinning, and the Fourier transform. Ridgelets have been developed recently and have many advantages over wavelets in applications to image processing. However, the current implementation of ridgelets cannot be applied to pattern recognition directly. In order to overcome this problem, we have successfully extracted ridgelet features within the circle surrounding the pattern we are trying to recognize. Wavelet cycle-spinning and Fourier spectrum magnitudes are used to achieve rotation invariance. The main motivation of using ridgelets is that we have a much better tool for the extraction of features based on line singularities as compared to point singularities as in the case of wavelets. Based on this observation, important features can be extracted. Our experiments show that our proposed descriptor is very robust to Gaussian noise and it achieves high recognition rates.
pattern recognition ridgelets wavelets cycle-spinning fourier transform feature extraction