@InBook{Matas-Unifying2008,
  IS = { zkontrolovano 16 Jan 2009 },
  UPDATE  = { 2008-08-28 },
  author =      {Matas, Ji{\v r}{\'i } and {\v S}ochman, Jan},
  title =       {Wald's Sequential Analysis for Time-constrained Vision Problems},
  booktitle =   {Unifying Perspectives in Computational and Robot Vision},
  year =        {2008},
  pages =       {57--77},
  chapter =     {5},
  editor =      {Kragic, Danica  and Kyrki, Ville},
  publisher =   {Springer},
  address =     {Springer Science+Business Media, LCC, 
                 233 Spring Street, NY 10013, New York, USA},
  isbn =        {978-0-387-75521-2},
  book_pages =  {212},
  series =      {Lecture Notes in Electrical Engineering},
  volume =      {8},
  authorship =  {50-50},
  annote =      {In detection and matching problems in computer vision,
    both classification errors and time to decision characterize the
    quality of an algorithmic solution. It is shown how to formalize such
    problems in the framework of sequential decision-making and derive
    quasi-optimal time-constrained solutions for three vision
    problems. The methodology is applied to face and interest point detection
    and to the RANSAC robust estimator. Error rates of the face
    detector proposed algorithm are comparable to the state-of-the-art
    methods. In the interest point application, the output of the
    Hessian-Laplace detector [Mikolajczyk-IJCV04] is approximated by a
    sequential WaldBoost classifier which is about five times faster
    than the original with comparable repeatability.  A sequential
    strategy based on Wald's SPRT for evaluation of model quality in
    RANSAC leads to significant speed-up in geometric matching
    problems.},
  keywords =    {sequential analysis, WaldBoost, RANSAC, interest points},
  project =     {FP6-IST-004176, FP6-IST-027113, GACR 201/06/1821},
}