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.
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页码：385 / +
页数：2
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