IS = { zkontrolovano 15 Dec 2007 },
  UPDATE  = { 2007-11-28 },
  author =     {{\v S}ochman, Jan and Matas, Ji{\v r}{\' \i}},
  title =      {Learning A Fast Emulator of a Binary Decision Process},
  year =       {2007},
  pages =      {236--245},
  booktitle =  {Computer Vision - ACCV 2007. Proceedings 8th Asian Conference on Computer Vision},
  editor =     {Yasushi Yagi and Sing Bing Kang and In So Kweon and Hongbin Zha},
  volume =     {II},
  address =    {Heidelberg, Germany},
  publisher =  {Springer},
  series =     {LNSC},
  isbn =       {978-3-540-76389-5},
  book_pages = {915},
  month =      {November},
  day =        {18-22},
  venue =      {Tokyo, Japan},
  annote = {Computation time is an important performance
    characteristic of computer vision algorithms. This paper shows how
    existing (slow) binary-valued decision algorithms can be
    approximated by a trained WaldBoost classifier, which minimises
    the decision time while guaranteeing predefined approximation
    precision. The core idea is to take an existing algorithm as a
    black box performing some useful binary decision task and to train
    the WaldBoost classifier as its emulator.  Two interest point
    detectors, Hessian-Laplace and Kadir-Brady saliency detector, are
    emulated to demonstrate the approach. The experiments show similar
    repeatability and matching score of the original and emulated
    algorithms while achieving a 70-fold speed-up for Kadir-Brady
  keywords =    {WaldBoost, interest points, Hessian-Laplace, saliency},
  prestige =    {international},
  authorship =  {50-50},
  project =     {FP6-IST-027113, GACR 102/07/1317},
psurl       = { [PDF, 1 MB], [PPT, 4 MB] },
 note =        {Sang Uk Lee Outstanding Paper Award},