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

Tamás Szirányi, Josiane Zerubia, László Czúni, David Geldreich, Zoltán Kato

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

A simplified statistical image segmentation algorithm for the cellular neural/nonlinear networks universal machine (CNN/UM), which is a new image processing tool, is developed. It contains thousands of cells with analog dynamics, local memories and processing units. The modified metropolis dynamics (MMD) optimization method is implemented into the raw analog architecture of the CNN-UM. Within the architecture, VLSI CNN chips can execute a pseudo-stochastic relaxation algorithm of about 100 iterations in about 100 μs.

Original languageEnglish
Pages (from-to)195-211
Number of pages17
JournalReal-Time Imaging
Volume6
Issue number3
Publication statusPublished - Jun 1 2000

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ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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