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

被引：1

作者：

Levada, Alexandre L. M. ^{[1
]}

Mascarenhas, Nelson D. A. ^{[2
]}

Tannus, Alberto ^{[1
]}

机构：

[1] Univ Sao Paulo, Phys Inst Sao Carlos, Trabalhador Saocarlense Ave 400,Postal Code 369, BR-13560970 Sao Carlos, SP, Brazil

[2] Univ Fed Sao Carlos, Dept Comp, BR-13565905 Sao Carlos, SP, Brazil

来源：

PROCEEDINGS OF IWSSIP 2008: 15TH INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING | 2008年

基金：

巴西圣保罗研究基金会;

关键词：

Markov random fields; Potts model; maximum pseudo-likelihood; stochastic image modeling;

D O I：

10.1109/IWSSIP.2008.4604447

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：385 / +

页数：2

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