Constrained sampling using simulated annealing

Authors:
Azadeh Mohebi;Paul Fieguth
Affiliations:
Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada;Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada
Venue:
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Year:
2007

Citing 3
Cited 0

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Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Posterior sampling of scientific images

ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

Scientific image processing involves a variety of problems including image modeling, reconstruction, and synthesis. In this paper we develop a constrained sampling approach for porous media synthesis and reconstruction in order to generate artificial samples of porous media. Our approach is different from current porous media reconstruction methods in which the Gibbs probability distribution is maximized by simulated annealing. We show that the artificial images generated by those methods do not contain the variability that typical samples of random fields are required to have.