@inbook {378,
	title = {Constrained Sampling Using Simulated Annealing},
	booktitle = {Image Analysis and Recognition},
	series = {Lecture Notes in Computer Science},
	volume = {4633},
	year = {2007},
	publisher = {Springer Berlin},
	organization = {Springer Berlin},
	address = {Heidelberg},
	abstract = {<p>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.</p>
},
	doi = {http://dx.doi.org/10.1007/978-3-540-74260-9_18},
	author = {A Mohebi and P Fieguth and M Kamel and A Campilho}
}
