Image segmentation using Markov random field model in fully parallel cellular network architectures

Markovian approaches to early vision processes need a huge amount of comput ing power. These algorithms call usually be implemented on parallel computi ng 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 mo del in parallel imaging VLSI chips. As an example, we have developed a simplified statistical image segmentatio n algorithm for the Cellular Neural/Nonlinear Networks Universal Machine (C NN-UM), which is a new image processing tool, containing thousands of cells with analog dynamics, local memories and processing units. The Modified Me tropolis Dynamics (MMD) optimization method can be implemented into the raw analog architecture of the CNN-UM. We can introduce the whole pseudo-stoch astic segmentation process in the CNN architecture using 8 memories/cell. W e use simple arithmetic functions (addition, multiplication), equality-test between neighboring pixels and very simple nonlinear output functions (ste p, jigsaw). With this architecture, the proposed VLSI CNN chip can execute a pseudo-stochastic relaxation algorithm of about 100 iterations in about 1 00 CLS. In the suggested solution the segmentation is unsupervised, where a pixel-l evel statistical estimation model is used. We have tested different monogri d and multigrid architectures. In our CNN-UM model several complex preprocessing steps can be involved, su ch as texture-classification or anisotropic diffusion. With these preproces sing steps, our fully parallel cellular system may work as a high-level ima ge segmentation machine, using only simple functions based on the close-nei ghborhood of a pixel. (C) 2000 Academic Press.