Understanding Shape using Probabilistic Correspondence

Physical objects in a given class often have a characteristic shape: we can all recognize a giraffe or a coffee mug even from a simple line drawing. This talk describes a characterization of object shape, both in 3D and in 2D, as a probabilistic graphical model, and demonstrates its application to problems in both vision and graphics. Our shape modeling framework encompasses signification variation both of general object shape and of object pose. We show how to learn this model from a collection of unlabeled instances of object shape. A key building block in this approach is the correspondence task, where we map points in the shape of one objects to the points in another. We describe a probabilistic formulation of this task and solutions for addressing it. We also present a method for automatically decomposing a shape into its articulated parts, and for learning a probabilistic model for its shape variation. Finally, we present applications of this framework to a variety of tasks. In the context of graphics, we show applications to shape completion and to shape synthesis from motion capture data. In the context of vision, we show how shape models can be used to precisely outline objects in a cluttered image. We also show how a semantically consistent shape model for an object class, learned from an unlabeled set of object shapes, can be used, with only a handful of labeled instances, to accurately answer semantic queries such as whether a cheetah is running or whether an airplane is taking off. Thus, a more detailed model of object shape can be used as a building block in semantic interpretation of the physical world.

Speaker Bio

Daphne Koller

Daphne Koller received her BSc and MSc degrees from the Hebrew University of Jerusalem, Israel, and her PhD from Stanford University in 1993. After a two-year postdoc at Berkeley, she returned to Stanford, where she is now a Professor in the Computer Science Department. Her main research interest is in developing and using machine learning and probabilistic methods to model and analyze complex domains. Her current research projects include models in computational biology and in reasoning about the physical worls. Daphne Koller is the author of over 100 refereed publications, which have appeared in venues spanning Science, Nature Genetics, the Journal of Games and Economic Behavior, and a variety of conferences and journals in AI and Computer Science. She was the program co-chair of the NIPS 2007 and UAI 2001 conferences, and has served on numerous program committees and as associate editor of the Journal of Artificial Intelligence Research and of the Machine Learning Journal. She was awarded the Arthur Samuel Thesis Award in 1994, the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001, the Cox Medal for excellence in fostering undergraduate research at Stanford in 2003, and the MacArthur Foundation Fellowship in 2004.