Knowledge-based part correspondence

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.

• Type: Journal
• Year: 2007
• Where: PR
• Authors: Boaz J. Super
• Keywords: Class Membership Information, Metric Distance Approach, PR 2007, Unseen Input Shapes