In this talk a class of approaches that model objects using hierarchical continuous-state graphical models will be presented. These approaches can be used to effectively model complex objects by allowing tractable and robust inference algorithms that are able to infer pose of these objects in the presence of realistic appearance variations and articulations. In these models that can be used to model both rigid and articulated object structures, nodes correspond to parts of objects and edges represent the constraints between parts encoded as statistical distributions. For articulated objects, these constraints can model spatial, temporal and occlusion relationships between parts. Localization, pose estimation, and tracking can then be formulated as inference in these graphical models. This formulation has a number of advantages over more traditional methods and can be used to solve the challenging problem of inferring the 3D pose of the person from single monocular image.
Bio: Leonid Sigal is a Ph.D. candidate under the supervision of Michael J. Black at Brown University (expected graduation in May 2007); he received his B.Sc. degrees in Computer Science and Mathematics from Boston University (1999), his M.A. from Boston University (1999), and his M.S. from Brown University (2003). From 1999 to 2001, he worked as a senior vision engineer at Cognex Corporation, where he developed industrial vision applications for pattern analysis and verification. In 2002, he spent a semester as a research intern at Siemens Corporate Research (SCR) working with Dorin Comaniciu on autonomous obstacle detection and avoidance for vehicle navigation. During the summers of 2005 and 2006, he worked as a research intern at Intel Applications Research Lab (ARL) on human pose estimation and tracking. His work received the Best Paper Award at the Articulate Motion and Deformable Objects Conference in 2006 (with Michael J. Black). Leonid's research interests are in computer vision and machine learning, including human motion analysis, graphical models, and probabilistic and hierarchical inference.