Rotation invariant feature extraction using Ridgelet and Fourier transforms

Authors:
Y. Chen;D. Bui;A. Krzyżak
Affiliations:
Department of Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve West, Montreal, QC, Canada;Department of Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve West, Montreal, QC, Canada;Department of Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve West, Montreal, QC, Canada
Venue:
Pattern Analysis & Applications
Year:
2006

Citing 0
Cited 5

Pattern recognition with SVM and dual-tree complex wavelets

Image and Vision Computing
Rotation invariant texture classification by ridgelet transform and frequency-orientation space decomposition

Signal Processing
Fast communication: Rotation-invariant texture retrieval using wavelet-based hidden Markov trees

Signal Processing
Radar target recognition using contourlet packet transform and neural network approach

Signal Processing
Invariant pattern recognition using contourlets and AdaBoost

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a novel descriptor for feature extraction by using a combination of Ridgelets and Fourier transform. We have successfully implemented ridgelets on the circular disk containing the pattern and applied Fourier transform on the resulting ridgelet coefficients to extract rotation-invariant features for pattern recognition. The descriptor is very robust to Gaussian noise even when the noise level is high. Experimental results show that the new descriptor is a very good choice for pattern recognition.