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Seminar: Model-Based Perceptual Grouping and Shape Abstraction
April 8, 2015 @ 10:00 am - 12:00 pm
Department of Computer Science
University of Toronto
For many object classes, shape is the most generic feature for object categorization. However, when a strong shape prior, i.e., a target object, is not available, domain independent, mid-level shape priors must play a critical role in not only grouping causally related features, but regularizing or abstracting them to yield higher-order shape features that support object categorization. In this talk, I will present a framework in which mid-level shape priors take the form of a vocabulary of simple, user-defined 2-D part models. From the vocabulary, we learn to not only group oversegmented regions into parts, but to abstract the shapes of the region groups, yielding a set of abstract part hypotheses. However, the process of shape abstraction can be thought of as a form of “controlled hallucination”, which comes at the cost of many competing 2-D part hypotheses. To improve part hypothesis precision, we assume that the 2-D parts represent the component faces of aspects that model a vocabulary of 3-D part models. We then exploit the relational structure (spatial context) of the faces encoded in the aspects, and again formulate hypothesis selection in a graph-theoretic, probabilistic framework. Finally, we introduce a technique that is able to recover the pose and shape of a volumetric part from a recovered aspect, yielding a framework that revisits the classical problem of recovering a set of qualitative 3-D volumetric parts from a single 2-D image.