IS = { zkontrolovano 14 Jan 2014 },
  UPDATE  = { 2013-01-25 },
  author =       {Lebeda, Karel},
  supervisor =   {Matas, Ji{\v r}{\'\i}},
  title =        {Robust Sample Consensus},
  school =       {Center for Machine Perception, K13133 FEE Czech Technical University},
  address =      {Prague, Czech Republic},
  year =         {2013},
  month =        {January},
  day =          {24},
  type =         {{MSc Thesis CTU--CMP--2013--01}},
  issn =         {1213-2365},
  pages =        {67},
  figures =      {19},
  authorship =   {100},
  psurl =        {[Lebeda-TR-2013-01.pdf]},
  project =      {GACR P103/12/2310, SGS11/125/OHK3/2T/13},
  annote =       {In this thesis, the problem of robust estimation of a
                  multiple view geometry in the computer vision is
                  studied. The main focus is put on random sampling techniques
                  for estimation of two-view geometries, in particular
                  homography and epipolar geometry, in a presence of
                  outliers. After a thorough analysis of LO-RANSAC, several
                  improvements are proposed to make it more robust to the
                  selection of the inlier/outlier error threshold and to the
                  number of points. A new estimator, faster, more accurate and
                  more robust that the state-of-the-art is the result. The
                  improvements were implemented in the framework of CMP
                  WBS-Demo and extensively tuned and experimentally tested on
                  diverse data, using a newly created testing framework. The
                  LO-RANSAC implementation for homography and epipolar
                  geometry estimation has been separated from the rest of
                  WBS-Demo and is now publicly available. The datasets were
                  made available as well, including new manually annotated
                  ground truth point correspondences.},
  keywords =     {computer vision, two-view geometry, robust estimation,
                  RANSAC, local optimization},