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Strong Markov random field model.
Journal article

Strong Markov random field model.

  • Paget R Computer Vision Group, Gloriastrasse 35, ETH-Zentrum, CH-8092 Zurich, Switzerland. rpaget@vision.ee.ethz.ch
  • 2004-09-21
Published in:
  • IEEE transactions on pattern analysis and machine intelligence. - 2004
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
English 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.
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://sonar.ch/global/documents/233757
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