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

Autor(es)

Levada, Alexandre L. M. ; Mascarenhas, Nelson D. A. ; Tannus, Alberto ; Rozinaj, G ; Cepko, J ; Truchly, P ; Vrabec, J ; Vojtko, J

• Tipo de documento: Artigo Científico
• Fonte: PROCEEDINGS OF IWSSIP 2008: 15TH INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING; 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 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. (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