@InProceedings{Cech-CVPR-2011,
  IS = { zkontrolovano 25 Jan 2014 },
  UPDATE  = { 2014-01-06 },
  author       = {Cech, Jan and Sanchez-Riera, Jordi and Horaud, Radu P.},
  title        = {Scene Flow Estimation by Growing Correspondence Seeds},
  booktitle    = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages        = {49-56},
  book_pages   =  {3503},
  day          = {20--25},
  month        = {June},
  year         = {2011},
  publisher    = {IEEE Computer Society Press},
  address       = {Los Alamitos, USA},
  editor =      {Felzenszwalb, Pedro and Forsyth, David and Fua, Pascal},
  ISBN         = {978-1-4577-0394-2},
  venue        = {Colorado Springs, CO, USA},
  url          = {http://perception.inrialpes.fr/Publications/2011/CSH11},
  doi = {10.1109/CVPR.2011.5995442},
  ANNOTE       = {A simple seed growing algorithm for estimating scene
                  flow in a stereo setup is presented. Two calibrated
                  and synchronized cameras observe a scene and output
                  a sequence of image pairs. The algorithm
                  simultaneously computes a disparity map between the
                  image pairs and optical flow maps between
                  consecutive images. This, together with calibration
                  data, is an equivalent representation of the 3D
                  scene flow, i.e. a 3D velocity vector is associated
                  with each reconstructed point. The proposed method
                  starts from correspondence seeds and propagates
                  these correspondences to their neighborhood. It is
                  accurate for complex scenes with large motions and
                  produces temporally-coherent stereo disparity and
                  optical flow results. The algorithm is fast due to
                  inherent search space reduction. An explicit
                  comparison with recent methods of spatiotemporal
                  stereo and variational optical and scene flow is
                  provided.},
  keywords     = {scene flow, disparity, optical flow, stereo, seed growing},
}