Convergence assessment techniques for Markov chain Monte Carlo
Statistics and Computing
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Statistics and Computing
Structural Approach for Building Reconstruction from a Single DSM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiband segmentation based on a hierarchical Markov model
Pattern Recognition
Marked point process for vascular tree extraction on angiogram
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We present a new method for the parametric decomposition of barred spiral galaxies in multispectral observations. The observation is modelled with a realistic image formation model and the galaxy is composed of physically significant parametric structures. The model also includes a parametric filtering to remove non-desirable aspects of the observation. Both the model and the filter parameters are estimated by a robust Monte Carlo Markov chain (MCMC) algorithm. The algorithm is based on a Gibbs sampler combined with a novel strategy of simulated annealing in which several temperatures allow to manage efficiently the simulation effort. Besides, the overall decomposition is performed following an original framework: a hierarchy of models from a coarse model to the finest one is defined. At each step of the hierarchy the estimate of a coarse model is used to initialize the estimation of the finer model. This leads to an unsupervised decomposition scheme with a reduced computation time. We have validated the method on simulated and real 5-band images: the results showed the accuracy and the robustness of the proposed approach.