by Zoltan Kato, Josiane Zerubia
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.
Reference:
Zoltan Kato, Josiane Zerubia, Markov random fields in image segmentation, of Foundations and Trends in Signal Processing, 2012, Now Publishers. (164 pages)
Bibtex Entry:
@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}
}