Classification of motion-blurred images using Zernike and wavelet-Fourier moments

C. Toxqui-Quitl; A. Padilla-Vivanco; F. Granados-Agustin

doi:10.1117/12.738233

24 October 2007 Classification of motion-blurred images using Zernike and wavelet-Fourier moments

C. Toxqui-Quitl, A. Padilla-Vivanco, F. Granados-Agustin

Proceedings Volume 6748, Image and Signal Processing for Remote Sensing XIII; 67481L (2007) https://doi.org/10.1117/12.738233
Event: SPIE Remote Sensing, 2007, Florence, Italy

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.

Citation Download Citation

C. Toxqui-Quitl, A. Padilla-Vivanco, and F. Granados-Agustin "Classification of motion-blurred images using Zernike and wavelet-Fourier moments", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67481L (24 October 2007); https://doi.org/10.1117/12.738233

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