Bayesian statistical models based on global constraints in computer vision and image processing

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My research program investigates the use of new unsupervised probabilistic or energy-based models including, as novelty, specific geometrical, functional, physiological or spatial adaptive global a priori constraints for restoring, reconstructing and understanding still and moving images.  In this way, this research program will attempt to propose a statistical framework to integrate multiple (low or higher level) image cues (e.g. edges, segmentation map, velocity field, depth map, etc.)  to improve the accuracy of the solution to be recovered or  to fuse multiple image cues coming from  multiple and complementary medical imaging modalities (SPECT with MRI, angiographic with Xrays, etc.) for the efficient restoration or the recognition or 3D reconstruction of anatomical objects or for the overall understanding of certain medical phenomenons. More generally, this research program aims at designing automatic recognition reconstruction and understanding systems of the next generations