@TechReport{Sochman-TR-2004-15,
  IS = { zkontrolovano 04 Mar 2005 },
  UPDATE  = { 2005-02-14 },
  author =       {{\v S}ochman, Jan and Matas, Ji{\v r}{\'\i}},
  title =        {{WaldBoost} -- Learning for Time Constrained Sequential
                  Detection},
  institution =  {Center for Machine Perception, K13133 FEE Czech Technical
                  University},
  address =      {Prague, Czech Republic},
  year =         {2004},
  month =        {October},
  type =         {Research Report},
  number =       {{CTU--CMP--2004--15}},
  issn =         {1213-2365},
  pages =        {9},
  figures =      {2},
  authorship =   {50-50},
  psurl =        {[Sochman-TR-2004-15
.pdf]},
  project =      {GACR 102/02/1539},
  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 =     {pattern recognition, computer vision, AdaBoost, 
                  face detection, sequential decision making},
}