Efficient bitmap resemblance under translations

Klara Kedem, Daniel Cohen

Research output: Contribution to journalArticlepeer-review


We present three efficient algorithms for resemblance between two bitmaps. Two of the algorithms are rasterized approximations, based on existing algorithms which compute the exact minimum Hausdorff distance between point sets under translation. The minimum Hausdorff distance is a min -max -min distance. We convert this operation into an and - or - and operation. This conversion, together with the fact that we encode distances into bits in the words of the pixel plane, of standard graphics hardware, contribute to the speed-up of our rasterized algorithms. The third algorithm is faster than the first two rasterized algorithms, and combines speed-up ideas from both. The performance of our rasterized algorithm is compared to an existing rasterized approximation algorithm for bitmap resemblance. We compare runtimes of these algorithms, parametrized by the size of the bitmap, the density of black bits in the bitmap and other parameters. Our results are summarized in tables and show that our algorithm is faster.

Original languageEnglish
Pages (from-to)57-74
Number of pages18
JournalInternational Journal of Computational Geometry and Applications
Issue number1-2
StatePublished - 1 Jan 1997


  • Bitmap
  • Geometric pattern matching
  • Minimum Hausdorff distance
  • Rasterized approximation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Geometry and Topology
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics


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