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- W2186273381 abstract "Thispaperpresents aprobabilistic approach for automatically segmenting foreground objects froma video sequence. A Bayesian network ispresented tomodeltheinter- actions amongthree variables, suchasforeground segmentation mask,motion segmentation field, andmotion vector field. Given twoconsecutive images, theconditional joint probability density ofthethree variables ismaximized iteratively tosimultaneously achieve foreground segmentation andmotionsegmentation in amutually beneficial manner. Thesolution totheoptimization problems areobtained byusingiterative conditional mode (ICM)andgraphcutalgorithm. Incorporating motionin- formation withbackground subtraction technique makesthe segmentation perform inamoresemantic level andobtain more accurate results. Experimental results fortwovideo sequences areprovided todemonstrate theeffectiveness oftheproposed approach." @default.
- W2186273381 created "2016-06-24" @default.
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- W2186273381 date "2006-01-01" @default.
- W2186273381 modified "2023-09-24" @default.
- W2186273381 title "A Bayesian Framework forForeground Segmentation" @default.
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