A stochastic dynamical system for image segmentation

Author

Ranjan, Uma S. ; Satyaranjan, Mohan

Author_Institution

Dept. of Electr. Eng., Indian Inst. of Sci., Bangalore, India

Volume

2

fYear

1997

fDate

26-29 Oct 1997

Firstpage

859

Abstract

Image segmentation is formulated as a stochastic process whose invariant distribution is concentrated at points of the desired region. By choosing multiple seed points, different regions can be segmented. The algorithm is based on the theory of time-homogeneous Markov chains and has been largely motivated by the technique of simulated annealing. The method proposed here has been found to perform well on real-world clean as well as noisy images while being computationally far less expensive than stochastic optimisation techniques

Keywords

Markov processes; image segmentation; parallel algorithms; simulated annealing; clean images; image segmentation; invariant distribution; multiple seed points; noisy images; region segmentation; simulated annealing; stochastic dynamical system; time-homogeneous Markov chains; Computational modeling; Cost function; Image edge detection; Image segmentation; Optimization methods; Parallel algorithms; Pixel; Simulated annealing; State-space methods; Stochastic processes; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1997. Proceedings., International Conference on

Conference_Location

Santa Barbara, CA

Print_ISBN

0-8186-8183-7

Type

conf

DOI

10.1109/ICIP.1997.638632

Filename

638632