Markov random field modeling in computer vision
Markov random field modeling in computer vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper extends and generalizes the Bayesian semi-supervised segmentation algorithm [1] for oil spill detection using SAR images. In the base algorithm on which we build on, the data term is modeled by a finite mixture of Gamma distributions. The prior is an M-level logistic Markov Random Field enforcing local continuity in a statistical sense. The methodology proposed in [1] assumes two classes and known smoothness parameter. The present work removes these restrictions. The smoothness parameter controlling the degree of homogeneity imposed on the scene is automatically estimated and the number of used classes is optional. Semi-automatic estimation of the class parameters is also implemented. The maximum a posteriori (MAP) segmentation is efficiently computed via the 驴驴 expansionalgorithm [2], a recent graph-cut technique, The effectiveness of the proposed approach is illustrated with simulated (Gaussian or Gamma data term and M-level logistic classes) and real ERS data.