Improving Potts MRF model parameter estimation in image analysis

Autor(es)

Levada, Alexandre L. M. ; Mascarenhas, Nelson D. A. ; Tannus, Alberto ; IEEE Computer Soc

• Tipo de documento: Artigo Científico
• Fonte: CSE 2008:11TH IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING, PROCEEDINGS; v. N/A, p. 2-pg., 2008-01-01.

Resumo

This paper presents a novel pseudo-likelihood equation for the estimation of the Potts MRF model parameter on second-order neighborhood systems. Experiments with simulated images comparing the proposed estimation method with a recent maximum-likelihood estimation approach derived in literature show the superiority of our methodology. In order to evaluate the performance of the estimation method, we proposed a hypothesis testing approach to validate the obtained results. The test statistic together with the p-values, calculated through our approximation for the asymptotic variance of maximum pseudo-likelihood estimators, provide a complete framework for quantitative analysis of Potts model parameter estimation in image processing, pattern recognition and computer vision applications using MRF models. (AU)

• Processo FAPESP: 06/01711-4 - Combinação de modelos de campos aleatórios markovianos para classificação contextual de imagens multiespectrais
• Beneficiário: Alexandre Luís Magalhães Levada
• Modalidade de apoio: Bolsas no Brasil - Doutorado