The representation and matching of categorical shape

  • Authors:

  • Ali Shokoufandeh;Lars Bretzner;Diego Macrini;M. Fatih Demirci;Clas Jönsson;Sven Dickinson

  • Affiliations:

  • Department of Computer Science, Drexel University, Philadelphia, PA;Computational Vision and Active Perception Laboratory, Department of Numerical Analysis and Computer Science, KTH, Stockholm, Sweden;Department of Computer Science, University of Toronto, Toronto, Ont., Canada;Department of Computer Science, Drexel University, Philadelphia, PA;Computational Vision and Active Perception Laboratory, Department of Numerical Analysis and Computer Science, KTH, Stockholm, Sweden;Department of Computer Science, University of Toronto, Toronto, Ont., Canada

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2006

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Abstract

We present a framework for categorical shape recognition. The coarse shape of an object is captured by a multiscale blob decomposition, representing the compact and elongated parts of an object at appropriate scales. These parts, in turn, map to nodes in a directed acyclic graph, in which edges encode both semantic relations (parent/child) as well as geometric relations. Given two image descriptions, each represented as a directed acyclic graph, we draw on spectral graph theory to derive a new algorithm for computing node correspondence in the presence of noise and occlusion. In computing correspondence, the similarity of two nodes is a function of their topological (graph) contexts, their geometric (relational) contexts, and their node contents. We demonstrate the approach on the domain of view-based 3-D object recognition.