@InProceedings{Matas-CVWW2005,
  IS = { zkontrolovano 06 Dec 2005 },
  UPDATE  = { 2005-09-05 },
  author =      {Matas, Ji{\v r}{\' \i} and Chum, Ond{\v r}ej },
  title =       {Optimal Randomised {RANSAC}},
  booktitle =   {Proceedings of the Computer Vision Winter Workshop 2005 (CVWW'05)},
  book_pages =  {223},
  pages =       {215--223},
  year =        {2005},
  month =       {February},
  editor =      {Allan Hanbury and Horst Bischof},
  publisher =   {PRIP TU Wien},
  address =     {Wien, Austria},
  day =         {2--4},
  venue =       {Zell an der Pram, Austria},
  project =     {CONEX GZ 45.535, BeNoGo IST-2001-39184,
                 MSMT Kontakt ME 678, STINT Dur IG2003-2 062,
                 MSM 6840770013},
  autorship =   {50-50},
  keywords =    {RANSAC, Wald, SPRT, Randomized RANSAC, robust geometry estimation},
  annote = {A randomized model verification strategy for RANSAC is
    presented.  The proposed method finds, like RANSAC, a solution
    that is optimal with user-controllable probability. A provably
    optimal model verification strategy is designed for the situation
    when the contamination of data by outliers is known, ie the
    algorithm is the fastest possible (on average) of all randomized
    RANSAC algorithms guaranteeing given confidence in the solution.
    The derivation of the optimality property is based on Wald's
    theory of sequential decision making. The RRANSAC with SPRT, which
    does not require the a priori knowledge of the fraction of
    outliers and has results close to the optimal strategy, is
    introduced.  We show experimentally that on standard test data the
    method is 2 to 10 times faster than the standard RANSAC and up to
    4 times faster than previously published methods. },
  psurl = {[Matas-CVWW2005.pdf]},
}