Stationary Markov random fields on a finite rectangular lattice

被引:16
作者
Champagnat, F
Idier, J
Goussard, Y
机构
[1] Supelec, Signaux & Syst Lab, F-91192 Gif Sur Yvette, France
[2] Ecole Polytech, Inst Genie Biomed, Stn Ctr Ville, Montreal, PQ H3C 3A7, Canada
关键词
Markov random fields; Pickard random fields; stationarity; unilaterality;
D O I
10.1109/18.737521
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 Z(2) 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 x 2) cell which must fulfill 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.
引用
收藏
页码:2901 / 2916
页数:16
相关论文
共 24 条
[1]  
BESAG J, 1974, J ROY STAT SOC B MET, V36, P192
[2]  
BESAG J, 1986, J R STAT SOC B, V48, P259
[3]   DISCRETE-INDEX MARKOV-TYPE RANDOM-PROCESSES [J].
DERIN, H ;
KELLY, PA .
PROCEEDINGS OF THE IEEE, 1989, 77 (10) :1485-1510
[4]  
DERIN H, 1984, IEEE T PATTERN ANAL, V6, P4
[5]  
Devijver P. A., 1988, Traitement du Signal, V5, P131
[6]  
Dobrusin R. L., 1968, THEOR PROBAB APPL, V13, P201
[7]  
GEMAN D, 1990, SPRINGER VERLAG LECT, V1427, P117
[8]   STOCHASTIC RELAXATION, GIBBS DISTRIBUTIONS, AND THE BAYESIAN RESTORATION OF IMAGES [J].
GEMAN, S ;
GEMAN, D .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1984, 6 (06) :721-741
[9]   UNILATERAL APPROXIMATION OF GIBBS RANDOM FIELD IMAGES [J].
GOUTSIAS, J .
CVGIP-GRAPHICAL MODELS AND IMAGE PROCESSING, 1991, 53 (03) :240-257
[10]   MUTUALLY COMPATIBLE GIBBS RANDOM-FIELDS [J].
GOUTSIAS, JK .
IEEE TRANSACTIONS ON INFORMATION THEORY, 1989, 35 (06) :1233-1249