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
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;
Journal_Title :
Signal Processing Letters, IEEE
