Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The authors present the class of semi-Markov random fields and deal, in particular, with the subclass of discrete-valued, nonsymmetric half-plane, unilateral Markov random fields. A hierarchical nonstationary-mean nonstationary-variance (NMNV) image model is proposed for the modeling of nonstationary and noisy images. This model seems to be advantageous as compared to a regular NMNV model because it statistically incorporates the correlation between pixels around the boundary of two adjacent regions. The hierarchical NMNV model leads to the development of an optimal algorithm that allows the simultaneous segmentation and model estimation of measured images. Although no theoretical result is available for the consistency of the estimated model parameters, the method seems to work sufficiently well for the examples considered.