Abstract
In order to determine the similarity between two planar shapes, which is an important problem in computer vision and pattern recognition, it is necessary to first match the two shapes as good as possible. As sets of allowed transformation to match shapes we consider translations, rigid motions, and similarities. We present a generic probabilistic algorithm based on random sampling for matching shapes which are modelled by sets of curves. The algorithm is applicable to the three considered classes of transformations. We analyze which similarity measure is optimized by the algorithm and give rigorous bounds on the number of samples necessary to get a prespecified approximation to the optimal match within a prespecified probability.
This research was supported by the European Union under contract No. IST-511572-2, Project PROFI, and by the Priority Programme 1307 “Algorithm Engineering” of Deutsche Forschungsgemeinschaft (DFG).
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Alt, H., Scharf, L. (2009). Shape Matching by Random Sampling . In: Das, S., Uehara, R. (eds) WALCOM: Algorithms and Computation. WALCOM 2009. Lecture Notes in Computer Science, vol 5431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00202-1_33
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DOI: https://doi.org/10.1007/978-3-642-00202-1_33
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