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In this paper, we present a study of transferable belief model for automatic hair segmentation process. Firstly, we recall the transferable Belief Model. Secondly, we defined for the parameters which characterize hair (Frequency and Color) a Basic Belief assignment which represents the belief that a pixel was or not a hair pixel. Then we introduce a discounting function based on the distance to the...
This paper presents a supervised foreground segmentation method that uses local and global feature similarity with edge constraint. This framework integrates and extends the notion of region growing and classification to deal with local and global fitness. It parameterizes constraint of growing using Chebyshev's inequality. The constraint is used to stop segmentation before matting. Matting relies...
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