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

Hunsop Hong; Dan Schonfeld

doi:10.1117/12.765000

28 January 2008 Attraction-repulsion expectation-maximization algorithm for image processing and sensor field networks

Hunsop Hong, Dan Schonfeld

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.

Citation Download Citation

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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KEYWORDS

Expectation maximization algorithms

Statistical analysis

Sensors

Reconstruction algorithms

Autoregressive models

Image restoration

Signal to noise ratio

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