DocumentCode :
870077
Title :
Strong Markov random field model
Author :
Paget, Rupert
Author_Institution :
Comput. Vision Group, Zurich, Switzerland
Volume :
26
Issue :
3
fYear :
2004
fDate :
3/1/2004 12:00:00 AM
Firstpage :
408
Lastpage :
413
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.
Keywords :
Markov processes; nonparametric statistics; texture; Markov random field; analysis-of-variance; checkerboard pattern; log linear construction formula; marginal distributions; nonparametric statistics; strong Markovian system; texture; Analysis of variance; Markov random fields; Mathematical model; Maximum likelihood estimation; Probability distribution; Statistical analysis; Statistical distributions; Virtual colonoscopy; 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;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2004.1262338
Filename :
1262338
Link To Document :
بازگشت