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Proceedings Article

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

[+] Author Affiliations
C. Toxqui-Quitl, F. Granados-Agustín

Instituto Nacional de Astrofísica, Óptica y Electrónica (Mexico)

A. Padilla-Vivanco

Univ. Politécnica de Tulancingo (Mexico)

Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67481L (October 24, 2007); doi:10.1117/12.738233
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From Conference Volume 6748

  • Image and Signal Processing for Remote Sensing XIII
  • Lorenzo Bruzzone
  • Florence, Italy | September 17, 2007

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.

© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

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


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