@InProceedings{pritts-icvnz14,
  IS = { zkontrolovano 25 Jan 2014 },
  UPDATE  = { 2014-01-06 },
  key =         {pritts-icvnz14},
  author =      {Pritts, James and Chum, Ond{\v r}ej and Matas, Ji{\v r}{\'\i}},
  title =       {Approximate Models for Fast and Accurate Epipolar Geometry Estimation},
  year =        {2013},
  pages =       {112-117},
  booktitle =   {2013 28th International Conference of Image and Vision Computing New Zealand (IVCNZ 2013)},
  editor =      {Taehyun Rhee, Ramesh Rayudu, Christopher Hollitt, John Lewis, Mengjie Zhang},
  publisher =   {IEEE},
  address =     {IEEE Operations Center, 445 Hoes Lane, Piscataway, USA},
  isbn =        {978-1-4799-0882-0},
  book_pages =  {517},
  month =       {November},
  day =         {27-29},
  venue =       {Wellington, New Zealand},
  annote =      {This paper investigates the plausibility of using
                  approximate models for hypothesis generation in a
                  RANSAC framework to accurately and reliably estimate
                  the fundamental matrix. Two novel fundamental matrix
                  estimators are introduced that sample two
                  correspondences to generate affine-fundamental
                  matrices for RANSAC hypotheses. A new RANSAC
                  framework is presented that uses local optimization
                  to estimate the fundamental matrix from the
                  consensus correspondence sets of verified hy-
                  potheses, which are approximate models. The proposed
                  estimators are shown to perform better than other
                  approximate models that have previously been used in
                  the literature for fundamental matrix estimation in
                  a rigorous evaluation. In addition the proposed
                  estimators are over 30 times faster, in terms of
                  models verified, than the 7-point method, and offer
                  comparable accuracy and repeatability on a large
                  subset of the test set.},
  keywords =    {Epipolar Geometry, RANSAC},
  project =     {GACR P103/12/2310, ERC-CZ LL1303, FP7-ICT-288587 MASELTOV},
}