@BOOK{Kato-Zerubia2012,
  title = {Markov random fields in image segmentation},
  publisher = {Now Publishers},
  year = {2012},
  author = {Zoltan Kato and Josiane Zerubia},
  series = {Foundations and Trends in Signal Processing},
  month = sep,
  note = {164 pages},
  abstract = {This monograph gives an introduction to the fundamentals of Markovian
	modeling in image segmentation as well as a brief overview of recent
	advances in the field. Segmentation is considered in a common framework,
	called image labeling, where the problem is reduced to assigning
	labels to pixels. In a probabilistic approach, label dependencies
	are modeled by Markov random fields (MRF) and an optimal labeling
	is determined by Bayesian estimation, in particular maximum a posteriori
	(MAP) estimation. The main advantage of MRF models is that prior
	information can be imposed locally through clique potentials. The
	primary goal is to demonstrate the basic steps to construct an easily
	applicable MRF segmentation model and further develop its multiscale
	and hierarchical implementations as well as their combination in
	a multilayer model. MRF models usually yield a non-convex energy
	function. The minimization of this function is crucial in order to
	find the most likely segmentation according to the MRF model. Besides
	classical optimization algorithms, like simulated annealing or deterministic
	relaxation, we also present recently introduced graph cutbased algorithms.
	We briefly discuss the possible parallelization techniques of simulated
	annealing, which allows efficient implementation on, e.g., GPU hardware
	without compromising convergence properties of the algorithms. While
	the main focus of this monograph is on generic model construction
	and related energy minimization methods, many sample applications
	are also presented to demonstrate the applicability of these models
	in real life problems such as remote sensing, biomedical imaging,
	change detection, and color- and motion-based segmentation. In real-life
	applications, parameter estimation is an important issue when implementing
	completely data-driven algorithms. Therefore some basic procedures,
	such as expectation-maximization, are also presented in the context
	of color image segmentation.},
  isbn = {978-1-60198-588-0}
}