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Recent developments in statistical theory and associated computational techniques have opened new avenues for image modeling as well as for image segmentation techniques. Thus, a host of models have been proposed and the ones which have probably received considerable attention are the hidden Markov fields (HMF) models. This is due to their simplicity of handling and their potential for providing improved image quality. Although these models provide satisfying results in the stationary case, they can fail in the nonstationary one. In this paper, we tackle the problem of modeling a nonstationary hidden random field and its effect on the unsupervised statistical image segmentation. We propose an original approach, based on the recent triplet Markov field (TMF) model, which enables one to deal with nonstationary class fields. Moreover, the noise can be correlated and possibly non-Gaussian. An original parameter estimation method which uses the Pearson system to find the natures of the noise margins, which can vary with the class, is also proposed and used to perform unsupervised segmentation of such images. Experiments indicate that the new model and related processing algorithm can improve the results obtained with the classical ones.
Triplet Markov fields, statistical image segmentation, paramater estimation, Pearson system, iterative conditional estimation, nonstationary images, textures classification.

W. Pieczynski and D. Benboudjema, "Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. , pp. 1367-1378, 2007.
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