Low-order graphical models for shapes and hierarchies in segmentation

This talks discusses simple (low-order) graphical models imposing practically powerful constraints on shapes and hierarchical structure of segments in the context of binary and multi-object labeling of images. We discuss properties and optimization for generic shape priors like "star", geodesic star, hedgehog, as well as models for partially-ordered labeling of interacting objects. While the talk focuses on biomedical applications where structural constraints (shapes and hierarchy) come from anatomy, the discussed general graphical models are useful for semi-supervised computer vision problems. Related papers appeared at CVPR 2017, 2016, ICCV 2009, 2005, ECCV 2008.