Strong Markov random field model

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 st...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2004-03, Vol.26 (3), p.408-413
Main Author: Paget, R.
Format: Article
Language:English
Subjects:
Algorithms
Analysis of variance
Artificial Intelligence
Computer Graphics
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Information Storage and Retrieval - methods
Markov Chains
Markov random fields
Mathematical model
Maximum likelihood estimation
Models, Statistical
Numerical Analysis, Computer-Assisted
Pattern Recognition, Automated
Probability distribution
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Statistical analysis
Statistical distributions
Subtraction Technique
Virtual colonoscopy
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2004.1262338