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Proceedings Paper

Curve matching in the framework of Riemannian geometry
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Paper Abstract

This paper addresses a fundamental problem in computer vision, curve matching. Curve matching and comparison play a key role in various applications. High-level vision problems usually require comparing curves, and the quality of tackling these problems relies much on the underlying curve matching techniques. Our goal is to define a distance on the space of plane (space) curves. The space of curves is taken as a manifold (topological space), and we consider Riemannian metrics on the manifold. The distance induced by a Riemannian metric is a metric, which, if not trivial, can be used as a similarity metric. This work also deals with the problem of partial curve matching given their starting points are known. Dynamic programming is used to implement partial matching, giving an efficient computational method. Experiments are conducted to test the distance invariant to translation and scaling.

Paper Details

Date Published: 19 January 2009
PDF: 8 pages
Proc. SPIE 7257, Visual Communications and Image Processing 2009, 725716 (19 January 2009); doi: 10.1117/12.805392
Show Author Affiliations
Yong Li, Univ. of Notre Dame (United States)
Robert L. Stevenson, Univ. of Notre Dame (United States)
Jiading Gai, Univ. of Notre Dame (United States)

Published in SPIE Proceedings Vol. 7257:
Visual Communications and Image Processing 2009
Majid Rabbani; Robert L. Stevenson, Editor(s)

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