@InProceedings{sochman-waldboost-cvpr05,
  IS = { zkontrolovano 30 Nov 2005 },
  UPDATE  = { 2005-07-18 },
author =      {{\v S}ochman, Jan and Matas, Ji{\v r}{\' \i}},
title =       {WaldBoost  - Learning for Time Constrained Sequential Detection},
booktitle =   {Proc. of Conference on Computer Vision and Pattern Recognition (CVPR)},
address =     {Los Alamitos, USA} ,
year =        {2005},
month =       {June},
day =         {20--25},
isbn        = {0-7695-2372-2},
publisher   = {IEEE Computer Society},
book_pages  = {1219},
pages    =    {150--157},
authorship =  {50-50},
psurl    =    {[pdf]},
project  =  {IST-004176, 1M0567},
annote = { In many computer vision classification problems, both the
  error and time characterizes the quality of a decision. We show that
  such problems can be formalized in the framework of sequential
  decision-making. If the false positive and false negative error
  rates are given, the optimal strategy in terms of the shortest
  average time to decision (number of measurements used) is the Wald's
  sequential probability ratio test (SPRT).  We built on the optimal
  SPRT test and enlarge its capabilities to problems with dependent
  measurements. We show, how the limitations of SPRT to a priori
  ordered measurements and known joint probability density functions
  can be overcome. We propose an algorithm with near optimal time -
  error rate trade-off, called WaldBoost, which integrates the
  AdaBoost algorithm for measurement selection and ordering and the
  joint probability density estimation with the optimal SPRT decision
  strategy.  The WaldBoost algorithm is tested on the face detection
  problem. The results are superior to the state-of-the-art methods in
  average evaluation time and comparable in detection rates.  },
keywords =    {Adaboost, cascade, Wald's SPRT, sequential analysis, face detection},
editor      = {Schmid, Cordelia and Soatto, Stefano and Tomasi, Carlo},
venue       = {San Diego, California, USA  },
volume      = { 2 },
}