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Proceedings Paper

Classification of motion-blurred images using Zernike and wavelet-Fourier moments
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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
Show Author Affiliations
C. Toxqui-Quitl, Instituto Nacional de Astrofísica, Óptica y Electrónica (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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