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>A VARIATIONAL FRAMEWORK FOR PARTIALLY OCCLUDED IMAGE SEGMENTATION USING COARSE TO FINE SHAPE ALIGNMENT AND SEMI-PARAMETRIC DENSITY APPROXIMATION
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A VARIATIONAL FRAMEWORK FOR PARTIALLY OCCLUDED IMAGE SEGMENTATION USING COARSE TO FINE SHAPE ALIGNMENT AND SEMI-PARAMETRIC DENSITY APPROXIMATION
In this paper, we propose a variational framework which combines top-down and bottom-up information to address the challenge of partially occluded image segmentation. The algorithm applies shape priors and divides shape learning into shape mode clustering and non-rigid transformation estimation to handle intraclass and interclass coarse to fine variations. A semi-parametric density approximation using adaptive meanshift and L2E robust estimation is used to model the likelihood. A set of real images is used to show the good performance of the algorithm.
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