Knowledge-based part correspondence

  • Authors:
  • Boaz J. Super

  • Affiliations:
  • Computer Science Department, University of Illinois at Chicago, Chicago, IL 60607, USA

  • Venue:
  • Pattern Recognition
  • Year:
  • 2007

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Abstract

This paper presents a direct method for finding corresponding pairs of parts between two shapes. Statistical knowledge about a large number of parts from many different objects is used to find a part correspondence between two previously unseen input shapes. No class membership information is required. The knowledge-based approach is shown to produce significantly better results than a classical metric distance approach. The potential role of part correspondence as a complement to geometric and structural comparisons is discussed.