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- W2978317375 abstract "A fast approach toobtain segmentation ofSAR images hasbeensuggested herebasedonthelocal statistical characteristics using MarkovRandomField (MRF)Modelon regionadjacency graph(RAG).First, an initially over- segmented imagederived fromthewatershed segmentation algorithm aswellastheoriginal SARimageistakenasthe inputs oftheproposed method. Secondly, aMRF isdefined onRAG oftheinitial oversegmented regions, withanovel multilevel logistic (MLL)modelfortheregion class labels andGamma distribution forthemarginal distribution of eachclass intheSARimages. Thecriterion usedforgetting theoptimal segmentation isthemaximization oftheposterior marginal (MPM),whichminimizing theexpected valueofthe numberofthemisclassified regions intheover-segmented image. Intheimplementation, theexpectation maximization (EM)algorithm isusedtoestimate theparameters ofGamma distribution, andtheparameters oftheMLL modelis derived fromtheRAG.Experimental results onrealSAR imagesshowthattheproposed methodcanreducethe computational complexity greatly and provideprecise segmentation results. Keywords-Image segmentation, MarkovRandomField (MRF),region adjacency graph(RAG),synthetic aperture radar(SAR)." @default.
- W2978317375 created "2019-10-10" @default.
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- W2978317375 date "2006-01-01" @default.
- W2978317375 modified "2023-09-24" @default.
- W2978317375 title "AnUnsupervisedSegmentation MethodUsing Markov Random Fieldon Region Adjacency Graph forSARImages" @default.
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- W2978317375 hasPublicationYear "2006" @default.
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