@article{miteran-jasp05,
  IS = { zkontrolovano 30 Nov 2005 },
  UPDATE  = { 2005-06-21 },
  Author      = { J. Miteran and J. Matas and E. Bourennane 
                  and M. Paindavoine and J. Dubois},
  Title       = { Hardware Implemplentation of a Discrete 
                  {A}da{B}oost {B}ased Decision Rule },
  Journal     = { Journal on Applied Signal Processing },
  Year        = { 2005 },
  Month       = { May  },
  Volume      = { 2005  },
  Number      = { 7 },
  Pages       = { 1035-1046},
  Publisher   = { Hindawi Publishing Corporation },
  Address     = { Sylvania, USA },
  ISSN        = { 1110-8657 },
  Keywords    = { Adaboost, hardware implementation  },
  project =     { 1ET101210407 },
  annote = { We propose a method and a tool for automatic generation
    of hardware implementation of a decision rule based on the
    Adaboost algorithm. We review the principles of the classification
    method and we evaluate its hardware implementation cost in terms
    of FPGA's slice, using different weak classifiers based on the
    general concept of hyperrectangle. The main novelty of our
    approach is that the tool allows the user to find automatically an
    appropriate tradeoff between classification performances and
    hardware implementation cost, and that the generated architecture
    is optimized for each training process. We present results
    obtained using Gaussian distributions and examples from UCI
    databases. Finally, we present an example of industrial
    application of real-time textured image segmentation.  },
}