Efficient Alignment and Correspondence Using Edit Distance

  • Paolo Bergamini
  • Luigi Cinque
  • Andrew D. J. Cross
  • Edwin R. Hancock
  • Stefano Levialdi
  • Richard Myers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)


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.


Delaunay Triangulation Edit Distance Computational Bottleneck Delaunay Graph Feasible Mapping 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Paolo Bergamini
    • 1
  • Luigi Cinque
    • 1
  • Andrew D. J. Cross
    • 2
  • Edwin R. Hancock
    • 2
  • Stefano Levialdi
    • 1
  • Richard Myers
    • 2
  1. 1.Dip. di Scienze dell’InformazioneUniversità “La Sapienza”di RomaVia SalariaRome, IT
  2. 2.Dept. of Computer ScienceUniversity of YorkYorkUK

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