Efficient alignment and correspondence using edit distance

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Abstract

This paper presents work aimed at rendering the dual-step EM algorithm of Cross and Hancock more efficient. The original algorithm integrates the processes of point-set alignment and correspondence. The consistency of the pattern of correspondence matches on the Delaunay triangulation of the points is used to gate contributions to the expected log-likelihood function for point-set alignment parameters. However, in its original form the algorithm uses a dictionary of structure-preserving mappings to asses the consistency of match. This proves to be a serious computational bottleneck. In this paper, we show how graph edit-distance can be used to compute the correspondence probabilities more efficiently. In a sensitivity analysis, we show that the edit distance method is not only more efficient, it is also more accurate than the dictionary-based method. © Springer-Verlag Berlin Heidelberg 2000.

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APA

Bergamini, P., Cinque, L., Cross, A. D. J., Hancock, E. R., Levialdi, S., & Myers, R. (2000). Efficient alignment and correspondence using edit distance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1876 LNCS, pp. 246–255). Springer Verlag. https://doi.org/10.1007/3-540-44522-6_26

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