Name
Title | ||
|---|---|---|
Image segmentation using Markov random field model in fully parallel cellular network architectures |
Abstract | ||
|---|---|---|
M arkovian 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 ms. In the suggested solution the segmentation is unsupervised, where a pixel-level statistical estimation model is used. We have tested diÄerent monogrid and multigrid architectures. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1006/rtim.1998.0159 | Real-Time Imaging |
Keywords | Field | DocType |
markov random field model,cellular network architecture,image segmentation,parallel computer,data representation,image processing,cellular network,arithmetic function,chip | Anisotropic diffusion,Scale-space segmentation,Markov random field,Segmentation,Computer science,Algorithm,Image processing,Image segmentation,Artificial neural network,Very-large-scale integration | Journal |
Volume | Issue | ISSN |
6 | 3 | Real-Time Imaging |
Citations | PageRank | References |
16 | 0.96 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tamás Szirányi | 1 | 152 | 26.92 |
| Josiane Zerubia | 2 | 2032 | 232.91 |
| László Czuni | 3 | 68 | 13.41 |
| David Geldreicch | 4 | 16 | 0.96 |
| Zoltan Kato | 5 | 265 | 28.28 |