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28 January 2008 Attraction-repulsion expectation-maximization algorithm for image processing and sensor field networks
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Proceedings Volume 6822, Visual Communications and Image Processing 2008; 68221E (2008) https://doi.org/10.1117/12.765000
Event: Electronic Imaging, 2008, San Jose, California, United States
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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hunsop Hong and Dan Schonfeld "Attraction-repulsion expectation-maximization algorithm for image processing and sensor field networks", Proc. SPIE 6822, Visual Communications and Image Processing 2008, 68221E (28 January 2008); https://doi.org/10.1117/12.765000
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