@InProceedings{zimmermann-accv2012,
  IS = { zkontrolovano 23 Jan 2014 },
  UPDATE  = { 2013-03-05 },
  author =      {Zimmermann, Karel and Hurych, David and Svoboda, Tom{\' a}{\v s}},
  title =       {Exploiting Features -- Locally Interleaved Sequential 
                 Alignment for Object Detection},
  year =        {2013},
  pages =       {437-450},
  booktitle =   {Computer Vision -- ACCV 2012, 11th Asian Conference on Computer Vision},
  editor =      {Lee, K.M. and Matsushita, Y. and Rehg, J.M. and Hu, Z.},
  publisher =   {Springer},
  address =     {Tiergartenstrasse 17, 69 121, Heidelberg, Germany},
  isbn =        {978-3-642-37330-5},
  issn = {0302-9743},
  volume =      {7724},
  book_pages =  {502},
  month =       {November},
  day =         {5-9},
  venue =       {Daejeon, Korea South},
  organization ={School of Engineering and Advanced Technology, Massey University},
  annote =      {We exploit image features multiple times in order to
    make sequential decision process faster and better performing. In
    the decision process features providing knowledge about the object
    presence or absence in a given detection window are successively
    evaluated. We show that these features also provide information
    about object position within the evaluated window. The
    classification process is sequentially interleaved with estimating
    the correct position. The position estimate is used for steering
    the features yet to be evaluated. This locally interleaved
    sequential alignment (LISA) allows to run an object detector on
    sparser grid which speeds up the process. The position alignment
    is jointly learned with the detector. We achieve a better
    detection rate since the method allows for training the detector
    on perfectly aligned image samples. For estimation of the
    alignment we propose a learnable regressor that approximates a
    non-linear regression function and runs in ne2076-1465gligible time.},
  keywords =    {exploit, features, adaboost, waldboost, sequential,
    regression, predictors, interleaved, alignment, sliding window},
  prestige =    {international},
  authorship =  {45-45-10},
  note =        {USB flash},
  mynote =      {Datum konani konference 7-9.11. 2012},
  project =     {GACR P103/11/P700, GACR P103/10/1585, FP7-ICT-247870 NIFTi},
  url =		{ftp://cmp.felk.cvut.cz/pub/cmp/articles/hurycd1/hurych-accv2012.pdf},
  doi = {10.1007/978-3-642-37331-2_34},
  psurl =       {[hurych-ACCV2012.pdf] },
}