
Proceedings Paper
Classification of motion-blurred images using Zernike and wavelet-Fourier momentsFormat | Member Price | Non-Member Price |
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Paper Abstract
In this paper, we consider the use of circular moments for invariant classification of images which have been blurred by
motion. The test images used here have been acquired when the objects are vibrating at different frequencies. A
comparative analysis using Zernike and Wavelet-Fourier moment sets is presented. An intensity normalization of the
input images is done to homogenize them due to inhomogeneous illumination produced by the acquisition. The
classification method is tested using images from objects which have intrinsically little differences between them.
Experimental results show that, the proposed classification method based in Zernike and Wavelet-Fourier moments can
be well addressed to grade images smeared by motion, from objects under high frequency vibrations.
Paper Details
Date Published: 24 October 2007
PDF: 10 pages
Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67481L (24 October 2007); doi: 10.1117/12.738233
Published in SPIE Proceedings Vol. 6748:
Image and Signal Processing for Remote Sensing XIII
Lorenzo Bruzzone, Editor(s)
PDF: 10 pages
Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67481L (24 October 2007); doi: 10.1117/12.738233
Show Author Affiliations
C. Toxqui-Quitl, Instituto Nacional de Astrofísica, Óptica y Electrónica (Mexico)
A. Padilla-Vivanco, Univ. Politécnica de Tulancingo (Mexico)
A. Padilla-Vivanco, Univ. Politécnica de Tulancingo (Mexico)
F. Granados-Agustin, Instituto Nacional de Astrofísica, Óptica y Electrónica (Mexico)
Published in SPIE Proceedings Vol. 6748:
Image and Signal Processing for Remote Sensing XIII
Lorenzo Bruzzone, Editor(s)
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