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...
Saved in:
Published in | 2007 IEEE International Conference on Systems, Man and Cybernetics pp. 1020 - 1025 |
---|---|
Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.10.2007
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
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 |