@Article{Zimmermann-Hurych-Svoboda-PAMI2014,
  IS = { zkontrolovano 07 Apr 2014 },
  UPDATE  = { 2014-04-07 },
  author =       {Zimmermann, Karel and Hurych, David and Svoboda,
                  Tom{\'a}{\v s}},
  title =        {Non-Rigid Object Detection with Local Interleaved
                  Sequential Alignment (LISA)},
  c_title =      {Detekce deformovan{\'y}ch objekt\accent23u pomoc{\'\i} lok{\'a}ln{\'\i}ho
                  sekven{\v c}n{\'\i}ho zarovn{\'a}v{\'a}n{\'\i} p{\v r}{\'\i}znak\accent23u},
  year =         {2014},
  month =        {April},
  pages =        {731-743},
  journal =      {Pattern Analysis and Machine Intelligence, IEEE
                  Transactions on},
  publisher =    {IEEE Computer Society},
  address =      {USA},
  issn =         {0162-8828},
  volume =       {36},
  number =       {4},
  authorship =   {40-40-20},
  annote =       {This paper shows that the successively evaluated
                  features used in a sliding window detection process
                  to decide about object presence/absence also contain
                  knowledge about object deformation. We exploit these
                  detection features to estimate the object
                  deformation. Estimated deformation is then
                  immediately applied to not yet evaluated features to
                  align them with the observed image data. In our
                  approach, the alignment estimators are jointly
                  learned with the detector. The joint process allows
                  for the learning of each detection stage from less
                  deformed training samples than in the previous
                  stage. For the alignment estimation we propose
                  regressors that approximate non-linear regression
                  functions and compute the alignment parameters
                  extremely fast.  },
  c_annote =     {{\v C}l{\'a}nek ukazuje, {\v z}e postupn{\'e}
                  vyhodnocovan{\'e} p{\v r}{\'\i}znaky mohou b{\'y}t
                  pou{\v z}ity nejen pro rozhodnut{\'\i}, zda na m{\'\i}st{\v e}
                  objekt je, ale tak{\'e} pro odhad deformace
                  objektu. Odhadnut{\'e} deformace je pr{\accent23u}b{\v e}{\v z}n{\v e}
                  aplikov{\'a}na b{\v e}hem sekven{\v c}n{\'\i}ho
                  rozhodovac{\'\i}ho procesu. Pozd{\v e}j{\v s}{\'\i}
                  p{\v r}{\'\i}znaky v rozhodovac{\'\i} sekvenci jsou
                  zarovn{\'a}ny/deformov{\'a}ny na z{\'a}klad{\v e}
                  pr{\accent23u}b{\v e}{\v z}n{\'e}ho odhadu deformace. Prediktory
                  deformac{\'\i} jsou u{\v c}eny sou{\v c}asn{\v e} se
                  sekven{\v c}n{\'\i}m klasifik{\'a}torem objektu. Pro
                  odhad deformace i rozhodnut{\'\i} o p{\'v r}{\'\i}tomnosti
                  objektu jsou pou{\v z}ity stejn{\'e}
                  p{\v r}{\'\i}znaky. Nau{\v c}en{\'e} neline{\'a}rn{\'\i}
                  prediktory deformac{\'\i} jsou aproximov{\'a}ny po
                  {\v c}{\'a}stech konstatn{\'\i} funkc{\'\i} a
                  implementov{\'a}ny ve form{\v e} tabulky.},
  keywords =     {Non-rigid object detection, alignment, regression,
                  exploiting features, real-time, waldboost, sliding
                  window, sequential decision process},
  project =      {GACR P103/11/P700, GACR P103/10/1585, FP7-ICT-247870 NIFTi},
  doi =          {10.1109/TPAMI.2013.171},
  ut_isi =       {: identifier of the publication in ISI (WoS) },
  psurl =        { [PDF] - revised version },
  www =          { http://dx.doi.org/10.1109/TPAMI.2013.171 },
}