Invariant pattern recognition using radon, dual-tree complex wavelet and Fourier transforms

An invariant pattern recognition descriptor is proposed in this paper by using the radon transform, the dual-tree complex wavelet transform and the Fourier transform. The radon transform can capture the directional features of the pattern image by projecting the pattern onto different orientation slices. The dual-tree complex wavelet transform can select shift-invariant features in a multiresolution way. The Fourier transform can extract features that are invariant to rotation of the patterns. Standard normalization techniques are used to normalize the input pattern image so that it is translation and scale invariant. Experiments conducted in this paper show that the proposed descriptor achieves high recognition rates for different combinations of rotation angles and noise levels. The descriptor is very robust to Gaussian white noise even when the noise level is very high.