Strong Markov random field model.

Rupert Paget

doi:10.1109/tpami.2004.1262338

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Strong Markov random field model.

Rupert Paget

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Abstract

The strong Markov random field (strong-MRF) model is a submodel of the more general MRF-Gibbs model. The strong-MRF model defines a system whose field is Markovian with respect to a defined neighborhood, and all subneighborhoods are also Markovian. A checkerboard pattern is a perfect example of a strong Markovian system. Although the strong Markovian system requires a more stringent assumption about the field, it does have some very nice mathematical properties. One mathematical property is the ability to define the strong-MRF model with respect to its marginal distributions over the cliques. Also, a direct equivalence to the Analysis-of-Variance (ANOVA) log-linear construction can be proven. From this proof, the general ANOVA log-linear construction formula is acquired.

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Algorithms Artificial Intelligence Computer Graphics Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Markov Chains Models, Statistical Numerical Analysis, Computer-Assisted Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted Subtraction Technique Comparative Study Evaluation Study Journal Article Validation Study

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Rupert Paget. 2004. Strong Markov random field model.. https://doi.org/10.1109/tpami.2004.1262338

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