DocumentCode :
1458403
Title :
Stationary Markov random fields on a finite rectangular lattice
Author :
Champagnat, Fréderic ; Idier, Jérôme ; Goussard, Yves
Author_Institution :
Lab. des Signaux et Syst. Supelec, Gif-sur-Yvette, France
Volume :
44
Issue :
7
fYear :
1998
fDate :
11/1/1998 12:00:00 AM
Firstpage :
2901
Lastpage :
2916
Abstract :
This paper provides a complete characterization of stationary Markov random fields on a finite rectangular (non-toroidal) lattice in the basic case of a second-order neighborhood system. Equivalently, it characterizes stationary Markov fields on Z2 whose restrictions to finite rectangular subsets are still Markovian (i.e., even on the boundaries). Until now, Pickard random fields formed the only known class of such fields. First, we derive a necessary and sufficient condition for Markov random fields on a finite lattice to be stationary. It is shown that their joint distribution factors in terms of the marginal distribution on a generic (2×2) cell which must fulfil some consistency constraints. Second, we solve the consistency constraints and provide a complete characterization of such measures in three cases. Symmetric measures and Gaussian measures are shown to necessarily belong to the Pickard class, whereas binary measures belong either to the Pickard class, or to a new nontrivial class which is further studied. In particular, the corresponding fields admit a simple parameterization and may be simulated in a simple, although nonunilateral manner
Keywords :
Gaussian processes; Markov processes; lattice theory; random processes; Gaussian measures; Pickard random fields; binary measures; consistency constraints; finite rectangular lattice; finite rectangular subsets; joint distribution; marginal distribution; nontoroidal lattice; second-order neighborhood system; stationary Markov random fields; symmetric measures; Euclidean distance; Image processing; Lattices; Layout; Markov random fields; Probability distribution; Stochastic processes; Sufficient conditions;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/18.737521
Filename :
737521
Link To Document :
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