@Article{Matas-IVC04b,
  IS = { zkontrolovano 13 Jan 2005 },
  UPDATE  = { 2004-10-01 },
  author =     {Matas, Ji{\v r}{\' \i} and Chum, Ond{\v r}ej},
  title =      {Randomized {RANSAC} with {$T_{d,d}$} test},
  year =       {2004},
  month =      {September},
  pages =      {837--842},
  journal =    {Image and Vision Computing},
  publisher =  {Elsevier Science},
  address =    {Amsterdam, The Netherlands},
  issn =       {0262-8856},
  volume =     {22},
  number =     {10},
  annote = {Many computer vision algorithms include a robust
    estimation step where model parameters are computed from a data
    set containing a significant proportion of outliers. The RANSAC
    algorithm is possibly the most widely used robust estimator in the
    field of computer vision. In the paper we show that under a broad
    range of conditions, RANSAC efficiency is significantly improved
    if its hypothesis evaluation step is randomized.},
  keywords =   {RANSAC, Randomized algorithm, 
                Epipolar geometry estimation},
  project =    {IST-2001-32184, GACR 102/02/1539, CTU 0306013, 
                CONEX GZ 45.535},
  psurl       = {DOI },
}