Abstract
This paper deals with the problem of unsupervised Bayesian segmentation of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (Simulated Annealing, ICM, etc...). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the available image only. Our approach consists of a recent iterative method of estimation, called Iterative Conditional Estimation (ICE), applied to a monogrid Markovian image segmentation model. The method has been tested on synthetic and real satellite images.
| Original language | English |
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| Pages (from-to) | 2399-2402 |
| Number of pages | 4 |
| Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Volume | 4 |
| Publication status | Published - 1 Jan 1995 |
| Externally published | Yes |
| Event | Proceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5) - Detroit, MI, USA Duration: 9 May 1995 → 12 May 1995 |