DocumentCode :
2151765
Title :
Rotation invariant feature extraction by combining denoising with Zernike moments
Author :
Chen, G.Y. ; Xie, W.F.
Author_Institution :
Center for Intell. Machines, McGill Univ., Montreal, QC, Canada
fYear :
2010
fDate :
11-14 July 2010
Firstpage :
186
Lastpage :
189
Abstract :
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.
Keywords :
Zernike polynomials; feature extraction; image classification; image denoising; image recognition; Zernike moment features; image classification rates; pattern image denoising; pattern recognition; rotation invariant feature extraction method; Image segmentation; Signal to noise ratio; Feature extraction; Zernike moments; denoising; pattern recognition; wavelet transform;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wavelet Analysis and Pattern Recognition (ICWAPR), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6530-9
Type :
conf
DOI :
10.1109/ICWAPR.2010.5576326
Filename :
5576326
Link To Document :
بازگشت