Stochastic simulation
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Inference for Spatial Processes
Statistical Inference for Spatial Processes
An iterative Gibbsian technique for reconstruction of m-ary images
Pattern Recognition
| Hi-index | 0.15 |
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=(X/sub i,j/) on a lattice L=((i,j): i=1,2,. . .,n; j=1,2,. . .,m). The marginal distributions of (X/sub i,j/,X/sub i+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*/sub i+u,j+v/)=b nearly equal to the estimate of P(X/sub i,j/=X/sub 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.