Abstract
This paper presents a comparative study of three deterministic unsupervised image segmentation algorithms. All of the three algorithms basically make use of a Markov random field (MRF) and try to obtain an approximate solution to the maximum likelihood (ML) or the maximum a posteriori (MAP) estimates. Although the three algorithms are based on the same stochastic image models, they adopt different ways to incorporate model parameter estimation into the iterative region label updating procedure. The differences among the three algorithms are identified and the convergence properties are compared both analytically and experimentally.
Original language | English |
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Pages (from-to) | 209-220 |
Number of pages | 12 |
Journal | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 2568 |
DOIs | |
State | Published - 11 Aug 1995 |
Event | Neural, Morphological, and Stochastic Methods in Image and Signal Processing 1995 - San Diego, United States Duration: 9 Jul 1995 → 14 Jul 1995 |
Keywords
- Expectation and maximization
- Gibbs random fields
- Iterated conditional modes
- Markov random fields
- Maximum a pesteriori estimation
- Maximum likelihood estimation
- Unsupervised image segmentation