2D Euclidean Distance Transform Algorithms: A Comparative Survey

2D Euclidean Distance Transform Algorithms: A Comparative Survey. Fabbri, R., Da, Torelli, J. C., & Bruno, O. M. ACM Computing Surveys, 40(1):1–44, February, 2008.
doi abstract bibtex

The distance transform (DT) is a general operator forming the basis of many methods in computer vision and geometry, with great potential for practical applications. However, all the optimal algorithms for the computation of the exact Euclidean DT (EDT) were proposed only since the 1990s. In this work, state-of-the-art sequential 2D EDT algorithms are reviewed and compared, in an effort to reach more solid conclusions regarding their differences in speed and their exactness. Six of the best algorithms were fully implemented and compared in practice.

@article{fabbri2DEuclideanDistance2008,
  title = {{{2D Euclidean}} Distance Transform Algorithms: A Comparative Survey},
  author = {Fabbri, Ricardo and {Da} and Torelli, Julio C. and Bruno, Odemir M.},
  year = {2008},
  month = feb,
  volume = {40},
  pages = {1--44},
  issn = {0360-0300},
  doi = {10.1145/1322432.1322434},
  abstract = {The distance transform (DT) is a general operator forming the basis of many methods in computer vision and geometry, with great potential for practical applications. However, all the optimal algorithms for the computation of the exact Euclidean DT (EDT) were proposed only since the 1990s. In this work, state-of-the-art sequential 2D EDT algorithms are reviewed and compared, in an effort to reach more solid conclusions regarding their differences in speed and their exactness. Six of the best algorithms were fully implemented and compared in practice.},
  journal = {ACM Computing Surveys},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-3646795,algorithms,comparison,data-transformation-modelling,distance-analysis,gridded-data},
  lccn = {INRMM-MiD:c-3646795},
  number = {1}
}

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