@string{iccv="Proceedings of International Conference on Computer Vision"}
@InProceedings{Kato-etal93a,
  author =	 {Kato, Zoltan and Berthod, Mark and Zerubia, Josiane},
  title =	 {Multiscale {M}arkov Random Field Models for Parallel
                  Image Classification},
  booktitle =	 iccv,
  pages =	 {253-257},
  year =	 1993,
  address =	 {Berlin, Germany},
  month =	 may,
  organization = {IEEE},
  ps =		 {../papers/iccv93.ps},
  abstract =	 {In this paper, we are interested in multiscale
                  Markov Random Field (MRF) models. It is well known
                  that multigrid methods can improve significantly the
                  convergence rate and the quality of the final
                  results of iterative relaxation techniques. Herein,
                  we propose a new hierarchical model, which consists
                  of a label pyramid and a whole observation
                  field. The parameters of the coarse grid can be
                  derived by simple computation from the finest
                  grid. In the label pyramid, we have introduced a new
                  local interaction between two neighbor grids. This
                  model gives a relaxation algorithm which can be run
                  in parallel on the entire pyramid. On the other
                  hand, the new model allows to propagate local
                  interactions more efficiently giving estimates
                  closer to the global optimum for deterministic as
                  well as for stochastic relaxation schemes. It can
                  also be seen as a way to incorporate cliques with
                  far apart sites for a reasonable price. }
}