Rotation invariant feature extraction by combining denoising with Zernike moments

Rotation invariant feature extraction is a classical topic in pattern recognition. It is well known that Zernike moment features are invariant with regard to rotation. However, due to noise present in the unknown pattern image, Zernike moment features can fail to recognize the noisy pattern. In this...

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Bibliographic Details
Published in2010 International Conference on Wavelet Analysis and Pattern Recognition pp. 186 - 189
Main Authors Chen, G Y, Xie, W F
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2010
Subjects
denoising
Feature extraction
Image segmentation
pattern recognition
Signal to noise ratio
wavelet transform
Zernike moments
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ISBN1424465303
9781424465309
ISSN2158-5695
DOI10.1109/ICWAPR.2010.5576326

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Summary:Rotation invariant feature extraction is a classical topic in pattern recognition. It is well known that Zernike moment features are invariant with regard to rotation. However, due to noise present in the unknown pattern image, Zernike moment features can fail to recognize the noisy pattern. In this paper, a new feature extraction method is proposed by combining a wavelet-based denoising method with zernike moment feature extraction in order to achieve improved classification rates. Experimental results demonstrate its superiority over zernike moments without denoising.
ISBN:1424465303
9781424465309
ISSN:2158-5695
DOI:10.1109/ICWAPR.2010.5576326