Transferable belief model for hair mask segmentation

Abstract : 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 face to increase the reliability of our sensors. At the end of this process, we segment the hair with a matting process. We compare the process with the logical fusion. Results are evaluated using semi-manual segmentation references
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Cedric Rousset, Pierre-Yves Coulon, Michèle Rombaut. Transferable belief model for hair mask segmentation. 17th IEEE International Conference on Image Processing (ICIP 2010), Sep 2010, Hong Kong, Hong Kong SAR China. pp.n.c. ⟨hal-00530156⟩

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