@string{rti="Real Time Imaging"}
@Article{Sziranyi-etal2000,
  author =	 {Sziranyi, Tamas and Zerubia, Josiane and Czuni,
                  Laszlo and Geldreich, David and Kato, Zoltan},
  title =	 {Image Segmentation Using {M}arkov Random Field Model
                  in Fully Parallel Cellular Network Architectures},
  journal =	 rti,
  year =	 2000,
  volume =	 6,
  number =	 3,
  pages =	 {195--211},
  month =	 jun,
  pdf =          {papers/rti2000.pdf},
  abstract =	 {Markovian approaches to early vision processes need
                  a huge amount of computing power. These algorithms
                  can usually be implemented on parallel computing
                  structures. Herein, we show that the Markovian
                  labeling approach can be implemented in fully
                  parallel cellular network architectures, using
                  simple functions and data representations. This
                  makes possible to implement our model in parallel
                  imaging VLSI chips. As an example, we have developed
                  a simplified statistical image segmentation
                  algorithm for the Cellular Neural/Nonlinear Networks
                  Universal Machine (CNN-UM), which is a new image
                  processing tool, containing thousands of cells with
                  analog dynamics, local memories and processing
                  units. The Modified Metropolis Dynamics (MMD)
                  optimization method can be implemented into the raw
                  analog architecture of the CNN-UM. We can introduce
                  the whole pseudo-stochastic segmentation process in
                  the CNN architecture using 8 memories/cell. We use
                  simple arithmetic functions (addition,
                  multiplication), equality-test between neighboring
                  pixels and very simple nonlinear output functions
                  (step, jigsaw). With this architecture, the proposed
                  VLSI CNN chip can execute a pseudo-stochastic
                  relaxation algorithm of about 100 iterations in
                  about 100 s. In the suggested solution the
                  segmentation is unsupervised, where a pixel-level
                  statistical estimation model is used. We have tested
                  different monogrid and multigrid architectures. In
                  our CNN-UM model several complex preprocessing steps
                  can be involved, such as texture-classification or
                  anisotropic diffusion. With these preprocessing
                  steps, our fully parallel cellular system may work
                  as a high-level image segmentation machine, using
                  only simple functions based on the
                  close-neighborhood of a pixel.}
}