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A simulation-based estimator for hidden Markov random fields

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1 Author(s)
A. Veijanen ; Dept. of Stat., Helsinki Univ., Finland

An estimator for estimating the parameters of a Markov random field X from inaccurate observations is introduced. The author considers first a Markov (Gibbs) random field X={Xi,j} on a lattice L={(i ,j): i=1,2,. . .,n; j=1,2,. . .,m}. The marginal distributions of (Xi,j, Xi+u,j+v) (u,v=-1,0,1) are first estimated from an image. Then, random fields X* are simulated with the probability of X*i+u,j+v)=b nearly equal to the estimate of P{Xi,j=X i+u,=b}. A simulation method similar to the Gibbs sampler is used. The parameters of the Markov random field model are estimated from the X*'s with the pseudolikelihood method

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:13 ,  Issue: 8 )