Description: Markov Random Fields in Image Segmentation by Zoltan Kato Introduces the fundamentals of Markovian modeling in image segmentation as well as providing a brief overview of recent advances in the field. Segmentation is considered in a common framework, called image labelling, where the problem is reduced to assigning labels to pixels. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description Markov Random Fields in Image Segmentation introduces the fundamentals of Markovian modeling in image segmentation as well as providing a brief overview of recent advances in the field. Segmentation is considered in a common framework, called image labelling, 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, this book also presents recently introduced graph cut-based algorithms. It discusses the possible parallelization techniques of simulated annealing, which allows efficient implementation on, for example, 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. Markov Random Fields in Image Segmentation is an essential companion for students, researchers and practitioners working on, or about to embark on research in statistical image segmentation. Table of Contents 1: Introduction 2: Markovian Segmentation Models 3: Classical Energy Minimization 4: Graph Cut 5: Parameter Estimation and Sample Applications 6: Conclusion. Acknowledgements. References Details ISBN1601985886 Author Zoltan Kato Short Title MARKOV RANDOM FIELDS IN IMAGE Language English ISBN-10 1601985886 ISBN-13 9781601985880 Media Book Format Paperback Year 2012 DEWEY 621.367 Pages 168 Imprint now publishers Inc DOI 10.1561/2000000035 Place of Publication Hanover Country of Publication United States Publication Date 2012-10-05 AU Release Date 2012-10-05 NZ Release Date 2012-10-05 US Release Date 2012-10-05 UK Release Date 2012-10-05 Publisher now publishers Inc Series Foundations and Trends® in Signal Processing Alternative 9781601985897 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:161893706;
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ISBN-13: 9781601985880
Book Title: Markov Random Fields in Image Segmentation
Number of Pages: 168 Pages
Language: English
Publication Name: Markov Random Fields in Image Segmentation
Publisher: Now Publishers Inc
Publication Year: 2012
Subject: Computer Science
Item Height: 234 mm
Item Weight: 246 g
Type: Textbook
Author: Zoltan Kato
Item Width: 156 mm
Format: Paperback
