DocumentCode :
1871780
Title :
Ridgelet moment invariants for pattern recognition
Author :
Guangyi Chen ; Gleason, Scott
Author_Institution :
Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, QC, Canada
fYear :
2012
fDate :
April 29 2012-May 2 2012
Firstpage :
1
Lastpage :
4
Abstract :
Moment invariants have been a hot research topic for several decades already. Even though existing moment invariants are good for applications like pattern recognition, there is still a need to further improve the existing moment invariants published in the literature. In this paper, a new set of invariant moments is proposed by using the ridgelet function, which is good at capturing line features in a pattern image. It has been proven that this set of moments is invariant to the rotation of pattern images. Experimental results show that the proposed ridgelet moment invariants are better than the Fourier-wavelet descriptor and Zernike´s moment invariants for pattern recognition under different rotation angles and different noise levels. It can be seen that the proposed ridgelet moment invariants can do an excellent job even when the noise levels are high.
Keywords :
AWGN; Fourier transforms; Zernike polynomials; edge detection; feature extraction; image denoising; optical character recognition; Fourier-wavelet descriptor; Gaussian white noise; Zernike moment invariants; line feature capture; noise levels; optical character recognition; pattern images rotation; pattern recognition; ridgelet function; ridgelet moment invariants; ridgelet transform; rotation angles; Noise level; Pattern recognition; Shape; Signal to noise ratio; Wavelet transforms; White noise; Gaussian white noise; Ridgelet transform; Zernike´s moments; optical character recognition (OCR); pattern recognition; ridgelet moments;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical & Computer Engineering (CCECE), 2012 25th IEEE Canadian Conference on
Conference_Location :
Montreal, QC
ISSN :
0840-7789
Print_ISBN :
978-1-4673-1431-2
Electronic_ISBN :
0840-7789
Type :
conf
DOI :
10.1109/CCECE.2012.6335061
Filename :
6335061
Link To Document :
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