@inproceedings{Matas-ICCV05,
  IS = { zkontrolovano 02 Jan 2006 },
  UPDATE  = { 2005-12-14 },
  author      = {Matas, Ji{\v r}{\' \i} and Chum, Ond{\v r}ej},
  title       = {Randomized RANSAC with Sequential Probability Ratio Test},
  year        = {2005},
  volume      = {II},
  pages       = {1727--1732},
  month       = {October},
  booktitle   = {Proc. IEEE International Conference on Computer Vision (ICCV)},
  keywords    = {RANSAC, SPRT, randomised verification, robust estimation, 
                 epipolar geometry, homography} ,
  project     = {1M0567, COSPAL IST-004176},
  publisher   = {IEEE Computer Society Press},
  address     = {New York, USA},
  isbn        = {0-7695-2334-X},
  book_pages  = {1876},
  editor      = {Ma, Songde and Shum, Heung-Yeung},
  day         = {15--21},
  venue       = {Hotel Beijing, Beijing, China},
  psurl       = { [pdf] },
  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, i.e. the algorithm is
    the fastest possible (on average) of all randomized RANSAC
    algorithms guaranteeing confidence in the solution. The derivation
    of the optimality property is based on Wald.s theory of sequential
    decision making. The R-RANSAC 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.},
}