IS = { zkontrolovano 29 Dec 2006 },
  UPDATE  = { 2006-09-12 },
  author =	 {{\v S}{\'a}ra, Radim},
  title =	 {Robust Correspondence Recognition for Computer Vision},
  year =	 {2006},
  pages =	 {119-131},
  booktitle =	 {COMPSTAT 2006: Proceedings in Computational Statistics of
                  17th ERS-IASC Symposium},
  editor =	 {Rizzi, Alfredo and Vichi, Maurizio},
  publisher =	 {Physica-Verlag},
  address =	 {Heidelberg, Germany},
  isbn =	 {3-7908-1708-2},
  book_pages =	 {537},
  month =	 {August/September},
  day =		 {28-1},
  venue =	 {Rome, Italy},
  organization = {European Regional Section of the International Association
                  for Statistical Computing},
  annote = {We introduce a new robust framework suitable for the task
    of finding correspondences in computer vision. If the problem
    domain is general enough, the correspondence problem can seldom
    employ any well-structured prior knowledge. This leads to tasks
    that have to find maximum cardinality solutions satisfying some
    weak optimality condition and a set of constraints. To avoid
    artifacts, robustness is required to cope with decision under
    occlusion, uncertainty or insufficiency of data and local
    violations of prior model. The proposed framework is based on a
    robust modification of graph-theoretic notion known as digraph
  keywords =	 {computer vision, robust matching, digraph kernel},
  project =	 {1ET101210406, MRTN-CT-2004-005439, FP6-IST-027113},