IS = { zkontrolovano 25 Jan 2014 },
  UPDATE  = { 2014-01-06 },
  title={USAC: A Universal Framework for Random Sample Consensus},
  author={Raguram, Rahul and Chum, Ond{\v r}ej and Pollefeys, Marc and Matas, Ji{\v r}{\'\i} and Frahm, Jan-{M}ichael},
 journal =      {{IEEE} Transactions on Pattern Analysis and Machine Intelligence},
  volume =       {35},
  number =       {8},
  month =        {August},
  publisher =    {IEEE Computer Society},
  address =      {New York, USA},
  issn =         {0162-8828},
  pages =        {2022--2038},
  keywords = {RANSAC, robust estimation},
  authorship =   {20-20-20-20-20},
doi = {10.1109/TPAMI.2012.257},
  project =      {GACR P103/12/2310, FP7-ICT-270138 DARWIN},
annote = {A computational problem that arises frequently in computer
                  vision is that of estimating the parameters of a
                  model from data that have been contaminated by noise
                  and outliers. More generally, any practical system
                  that seeks to estimate quantities from noisy data
                  measurements must have at its core some means of
                  dealing with data contamination. The random sample
                  consensus (RANSAC) algorithm is one of the most
                  popular tools for robust estimation. Recent years
                  have seen an explosion of activity in this area,
                  leading to the development of a number of techniques
                  that improve upon the efficiency and robustness of
                  the basic RANSAC algorithm. In this paper, we
                  present a comprehensive overview of recent research
                  in RANSAC-based robust estimation by analyzing and
                  comparing various approaches that have been explored
                  over the years. We provide a common context for this
                  analysis by introducing a new framework for robust
                  estimation, which we call Universal RANSAC
                  (USAC). USAC extends the simple
                  hypothesize-and-verify structure of standard RANSAC
                  to incorporate a number of important practical and
                  computational considerations. In addition, we
                  provide a general-purpose C++ software library that
                  implements the USAC framework by leveraging
                  state-of-the-art algorithms for the various
                  modules. This implementation thus addresses many of
                  the limitations of standard RANSAC within a single
                  unified package. We benchmark the performance of the
                  algorithm on a large collection of estimation
                  problems. The implementation we provide can be used
                  by researchers either as a stand-alone tool for
                  robust estimation or as a benchmark for evaluating
                  new techniques.},