IS = { zkontrolovano 16 Jan 2009 },
  UPDATE  = { 2008-08-28 },
  author =       {Chum, Ond{\v r}ej and Matas, Ji{\v r}{\' i}},
  title =        {Optimal Randomized RANSAC},
  journal =      {{IEEE} Transactions on Pattern Analysis and Machine Intelligence},
  volume =       {30},
  number =       {8},
  year =         {2008},
  month =        {August},
  publisher =    {IEEE Computer Society},
  address =      {Los Alamitos, USA},
  issn =         {0162-8828},
  pages =        {1472-1482},
  authorship =   {50-50},
  project =      {GACR 102/07/1317},
  keywords =     {RANSAC, randomized RANSAC},
  annote = {A randomized model verification strategy for RANSAC is
    presented. The proposed method finds, like RANSAC, a solution that
    is optimal with user-specified probability. The solution is found
    in time that is close to the shortest possible and superior to any
    deterministic verification strategy. A provably fastest model
    verification strategy is designed for the (theoretical) situation
    when the contamination of data by outliers is known. In this case,
    the algorithm is the fastest possible (on the average) of all
    randomized RANSAC algorithms guaranteeing a confidence in the
    solution. The derivation of the optimality property is based on
    Wald's theory of sequential decision making, in particular, a
    modified sequential probability ratio test (SPRT). Next, the
    R-RANSAC with SPRT algorithm is introduced. The algorithm removes
    the requirement for a priori knowledge of the fraction of outliers
    and estimates the quantity online.  We show experimentally that on
    standard test data, the method has performance close to the
    theoretically optimal and is 2 to 10 times faster than standard
    RANSAC and is up to four times faster than previously published