Feature Extraction Using Radon, Wavelet and Fourier Transform

In this paper, we propose a novel descriptor for invariant pattern recognition by using the Radon transform, the 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. Th...

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Bibliographic Details
Published in2007 IEEE International Conference on Systems, Man and Cybernetics pp. 1020 - 1025
Main Authors Chen, G.Y., Kegl, B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2007
Subjects
Computational complexity
Discrete transforms
Feature extraction
Fourier transform
Fourier transforms
Noise level
Noise robustness
Pattern recognition
Pixel
Radon transform
wavelet transform
Wavelet transforms
White noise
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Summary:In this paper, we propose a novel descriptor for invariant pattern recognition by using the Radon transform, the 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 combination of the 2-D shift invariant wavelet transform with 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.
ISBN:142440990X
9781424409907
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2007.4413718