Matching with PROSAC - Progressive Sample Consensus

Abstract

Keywords

Bibtex entry

@InProceedings{chum-prosac-cvpr05,
author =      {Chum, Ond{\vr}ej and Matas, Ji{\vr}{\'i}},
title =       {Matching with {PROSAC} -  Progressive Sample Consensus},
booktitle =   {Proc. of Conference on Computer Vision and Pattern Recognition (CVPR)},
address =     {Los Alamitos, USA} ,
year =        {2005},
month =       {June},
day =         {20--25},
isbn        = {0-7695-2372-2},
publisher   = {IEEE Computer Society},
pages    =    {220--226},
annote = { A new robust matching method is proposed. The Progressive
  Sample Consensus (PROSAC) algorithm exploits the linear ordering
  defined on the set of correspondences by a similarity function used
  in establishing tentative correspondences. Unlike RANSAC, which
  treats all correspondences equally and draws random samples
  uniformly from the full set, PROSAC samples are drawn from
  progressively larger sets of top-ranked correspondences. Under the
  mild assumption that the similarity measure predicts correctness of
  a match better than random guessing, we show that PROSAC achieves
  large computational savings. Experiments demonstrate it is often
  significantly faster (up to more than hundred times) than
  RANSAC. For the derived size of the sampled set of correspondences
  as a function of the number of samples already drawn, PROSAC
  converges towards RANSAC in the worst case. The power of the method
  is demonstrated on widebaseline matching problems.  },
keywords =    {RANSAC, wide-baseline stereo},
editor      = {Schmid, Cordelia and Soatto, Stefano and Tomasi, Carlo},
venue       = {San Diego, California, USA  },
volume      = { 1 },
}