The recognition of 3-D objects from their silhouettes demands a shape representation which is invariant to minor changes in viewpoint and articulation. This invariance can be achieved by parsing a silhouette into parts and relationships that are stable across similar object views. Medial descriptions, such as skeletons and shock graphs, attempt to decompose a shape into parts, but suffer from instabilities that lead to similar shapes being represented by dissimilar part sets. In this talk, I will present a novel shape parsing approach based on identifying and regularizing the ligature structure of a given medial axis. The result of this process is a bone graph, a new medial shape abstraction that captures a more intuitive notion of an object's parts than a skeleton or a shock graph, and offers improved stability and within-class deformation invariance. In addition, I will present a novel DAG matching algorithm that treats the similarity of node and edge attributes as a function of the hierarchical node constraints encoded in each graph. This algorithm can be used to compare bone graphs and shock graphs in the presence of noise, occlusion, and clutter. Finally, I will demonstrate the bone graph representation and the graph matching algorithm for the task of object categorization.

Biography:

Diego Macrini received an Engineering degree in software engineering from the Universidad de Belgrano (Buenos Aires), in 1998, and a M.Sc. degree in computer science from the University of Toronto, in 2003, where he is currently pursuing a Ph.D. degree in the same area. His major field of interest is computer vision with an emphasis on shape representation, object recognition, and visual motion analysis.