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- W3003417334 abstract "This paper addresses a fundamental problem in computer vision, curve matching. Curve matching and comparison play akey role in various applications. High-level vision problems usually require comparing curves, and the quality of tacklingthese problems relies much on the underlying curve matching techniques. Our goal is to define a distance on the space ofplane (space) curves. The space of curves is taken as a manifold (topological space), and we consider Riemannian metricson the manifold. The distance induced by a Riemannian metric is a metric, which, if not trivial, can be used as a similaritymetric. This work also deals with the problem of partial curve matching given their starting points are known. Dynamicprogramming is used to implement partial matching, giving an efficient computational method. Experiments are conductedto test the distance invariant to translation and scaling." @default.
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- W3003417334 date "2009-01-18" @default.
- W3003417334 modified "2023-09-25" @default.
- W3003417334 title "Curve matching in the framework of Riemannian geometry" @default.
- W3003417334 doi "https://doi.org/10.1117/12.805392" @default.
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