Cellular neural network for Markov random field image segmentation

Tamas Sziranyi, Josiane Zerubia, David Geldreich, Zoltan Kato

Research output: Paper

5 Citations (Scopus)

Abstract

Statistical approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. Although CNN is a fast parallel processor array for image processing, it is basically a deterministic analog circuit. The present work demonstrates the use of the CNN-UM architecture for statistical image segmentation.

Original languageEnglish
Pages139-144
Number of pages6
Publication statusPublished - dec. 1 1996
EventProceedings of the 1996 4th IEEE International Workshop on Cellular Neural Networks, and Their Applications, CNNA-96 - Seville, Spain
Duration: jún. 24 1996jún. 26 1996

Other

OtherProceedings of the 1996 4th IEEE International Workshop on Cellular Neural Networks, and Their Applications, CNNA-96
CitySeville, Spain
Period6/24/966/26/96

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ASJC Scopus subject areas

  • Software

Cite this

Sziranyi, T., Zerubia, J., Geldreich, D., & Kato, Z. (1996). Cellular neural network for Markov random field image segmentation. 139-144. Paper presented at Proceedings of the 1996 4th IEEE International Workshop on Cellular Neural Networks, and Their Applications, CNNA-96, Seville, Spain, .