Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelets: a tutorial in theory and applications
Wavelets: a tutorial in theory and applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Point Correspondence for Image Registration Using Optimization with Extremal Dynamics
Proceedings of the 24th DAGM Symposium on Pattern Recognition
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The two major Markov Random Fields (MRF) based algorithms for image segmentation are the Simulated Annealing (SA) and Iterated Conditional Modes (ICM). In practice, compared to the SA, the ICM provides reasonable segmentation and shows robust behavior in most of the cases. However, the ICM strongly depends on the initialization phase. In this paper, we combine Bak-Sneppen model and Markov Random Fields to define a new image segmentation approach. We introduce a multiresolution technique in order to speed up the segmentation process and to improve the restoration process. Image pixels are viewed as lattice species of Bak-Sneppen model. The a-posteriori probability corresponds to a local fitness. At each cycle, some objectionable species are chosen for a random change in their fitness values. Furthermore, the change in the fitness of each species engenders fitness changes for its neighboring species. After a certain number of iteration, the system converges to a Maximum A Posteriori estimate. In this multireolution approach, we use a wavelet transform to reduce the size of the system.