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 L(2)E robust estimation is used to model the likelihood. A set of real images is used to show the good performance of the algorithm.