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• W2163892884 abstract "This paper presents a new Bayesian supervised segmentation algorithm aimed at oil spill detection in SAR images, a crucial step in any SAR based automatic oil spill surveillance system. The data term, i.e., the density of the observed amplitudes given the region, is modeled as a finite mixture of Gamma distributions. This renders robustness to backscattering fluctuations inside each region. The prior is an M-level Markov Random Field defined on a 2D grid, enforcing local continuity in a statistical sense. The maximum a posteriori (MAP) segmentation is computed efficiently by means of recent graph-cut techniques. The effectiveness of the proposed method is illustrated with real ERS and Envisat data." @default.
• W2163892884 created "2016-06-24" @default.
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• W2163892884 date "2006-04-01" @default.
• W2163892884 modified "2023-09-24" @default.
• W2163892884 title "Bayesian Adaptive Oil Spill Segmentation of SAR Images via Graph Cuts" @default.
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