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Rotation invariant feature extraction using Ridgelet and Fourier transforms

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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.

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Acknowledgments

The authors would like to thank the anonymous reviewers and the Associate Editor whose constructive comments and suggestions have improved the quality of the paper. This work was supported by research grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) and by the Fonds Quebecois de la Recherche sur la Nature et les Technologies (FQRNT) of Quebec.

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve West, Montreal, QC, Canada, H3G 1M8

    G. Y. Chen, T. D. Bui & A. Krzyżak

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  1. G. Y. Chen
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  2. T. D. Bui
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  3. A. Krzyżak
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Correspondence to G. Y. Chen.

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Chen, G.Y., Bui, T.D. & Krzyżak, A. Rotation invariant feature extraction using Ridgelet and Fourier transforms. Pattern Anal Applic 9, 83–93 (2006). https://doi.org/10.1007/s10044-006-0028-8

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