DocumentCode
1391291
Title
A genetic algorithm-based segmentation of Markov random field modeled images
Author
Kim, E.Y. ; Park, S.H. ; Kim, H.J.
Author_Institution
Dept. of Comput. Eng., Kyungpook Nat. Univ., Taegu, South Korea
Volume
7
Issue
11
fYear
2000
Firstpage
301
Lastpage
303
Abstract
An unsupervised method is presented for segmenting video sequences degraded by noise. Each frame in a sequence is modeled using a Markov random field (MRF), and the energy function of each MRF is minimized by chromosomes that evolve using distributed genetic algorithms. To improve the computational efficiency, only unstable chromosomes corresponding to moving object parts are evolved. Experimental results show the effectiveness of the proposed method.
Keywords
Markov processes; distributed algorithms; genetic algorithms; image segmentation; image sequences; video signal processing; Markov random field model; computational efficiency; distributed genetic algorithms; energy function; image segmentation; moving object parts; noise degradation; unstable chromosomes; unsupervised method; video sequences; Biological cells; Computational complexity; Computational efficiency; Degradation; Genetic algorithms; Image segmentation; Markov random fields; Robustness; Space exploration; Video sequences;
fLanguage
English
Journal_Title
Signal Processing Letters, IEEE
Publisher
ieee
ISSN
1070-9908
Type
jour
DOI
10.1109/97.873564
Filename
873564
Link To Document