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Improving Potts MRF model parameter estimation using higher-order neighborhood systems on stochastic image modeling

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3 Author(s)
Alexandre L. M. Levada ; Physics Institute of São Carlos, University of São Paulo, Trabalhador Sãocarlense Avenue, 400, Postal Code 369, Zip Code 13560-970, Brazil ; Nelson D. A. Mascarenhas ; Alberto Tannus

This paper presents a novel pseudo-likelihood equation for the estimation of the Potts MRF model parameter on third-order neighborhood systems, allowing the modeling of less restrictive contextual systems in a large number of MRF applications in a computationally feasible way. The evaluation is done by a hypothesis testing approach using our approximation for the maximum pseudo-likelihood (MPL) estimator asymptotic variance. The test statistics together with the p-values, provide a complete framework for quantitative analysis in MRF parameter estimation on stochastic image modeling.

Published in:

2008 15th International Conference on Systems, Signals and Image Processing

Date of Conference:

25-28 June 2008