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Journal of Electronic Imaging

Gaussian mixture modeling approach for stationary human identification in through-the-wall radar imagery
Author(s): Vamsi Kilaru; Moeness G. Amin; Fauzia Ahmad; Pascale Sévigny; David D. J. DiFilippo
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Paper Abstract

We propose a Gaussian mixture model (GMM)-based approach to discriminate stationary humans from their ghosts and clutter in through-the-wall radar images. More specifically, we use a mixture of Gaussian distributions to model the image intensity histograms corresponding to target and ghost/clutter regions. The mixture parameters, namely the means, variances, and weights of the component distributions, are used as features and a K-nearest neighbor classifier is employed. The performance of the proposed method is evaluated using real-data measurements of multiple humans standing or sitting at different locations in a small room. Experimental results show that the nature of the targets and ghosts/clutter in the image allows successful application of the GMM feature-based classifier to distinguish between target and ghost/clutter regions.

Paper Details

Date Published: 17 February 2015
PDF: 11 pages
J. Electron. Imag. 24(1) 013028 doi: 10.1117/1.JEI.24.1.013028
Published in: Journal of Electronic Imaging Volume 24, Issue 1
Show Author Affiliations
Vamsi Kilaru, Villanova Univ. (United States)
Moeness G. Amin, Villanova Univ. (United States)
Fauzia Ahmad, Villanova Univ. (United States)
Pascale Sévigny, Defence Research and Development Canada (Canada)
David D. J. DiFilippo, Defence Research and Development Canada (Canada)

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