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Proceedings Paper

Attraction-repulsion expectation maximization algorithm for image processing and sensor field networks
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Paper Abstract

An attraction-repulsion expectation-maximization (AREM) algorithm for density estimation is proposed in this paper. We introduce a Gibbs distribution function for attraction and inverse Gibbs distribution for repulsion as an augmented penalty function in order to determine equilibrium between over-smoothing and over-fitting. The logarithm of the likelihood function augmented the Gibbs density mixture is solved under expectation-maximization (EM) method. We demonstrate the application of the proposed attraction-repulsion expectation-maximization algorithm to image reconstruction and sensor field estimation problem using computer simulation. We show that the proposed algorithm improves the performance considerably.

Paper Details

Date Published: 28 January 2008
PDF: 9 pages
Proc. SPIE 6822, Visual Communications and Image Processing 2008, 68221E (28 January 2008); doi: 10.1117/12.765000
Show Author Affiliations
Hunsop Hong, Univ. of Illinois at Chicago (United States)
Dan Schonfeld, Univ. of Illinois at Chicago (United States)

Published in SPIE Proceedings Vol. 6822:
Visual Communications and Image Processing 2008
William A. Pearlman; John W. Woods; Ligang Lu, Editor(s)

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