Name
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 |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/ICIP.1997.638632 | Image Processing, 1997. Proceedings., International Conference |
Keywords | Field | DocType |
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 | Simulated annealing,Computer vision,Markov process,Scale-space segmentation,Pattern recognition,Computer science,Parallel algorithm,Markov chain,Stochastic process,Image segmentation,Artificial intelligence,Invariant (mathematics) | Conference |
Volume | ISBN | Citations |
2 | 0-8186-8183-7 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Uma S. Ranjan | 1 | 0 | 0.34 |
| Satyaranjan, M. | 2 | 0 | 0.34 |