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.