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Image Analysis, Random Fields and Dynamic Monte Carlo Methods

Image Analysis, Random Fields and Dynamic Monte Carlo Methods
Author: Gerhard Winkler
Publisher: Springer Science & Business Media
Total Pages: 321
Release: 2012-12-06
Genre: Mathematics
ISBN: 3642975224

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This text is concerned with a probabilistic approach to image analysis as initiated by U. GRENANDER, D. and S. GEMAN, B.R. HUNT and many others, and developed and popularized by D. and S. GEMAN in a paper from 1984. It formally adopts the Bayesian paradigm and therefore is referred to as 'Bayesian Image Analysis'. There has been considerable and still growing interest in prior models and, in particular, in discrete Markov random field methods. Whereas image analysis is replete with ad hoc techniques, Bayesian image analysis provides a general framework encompassing various problems from imaging. Among those are such 'classical' applications like restoration, edge detection, texture discrimination, motion analysis and tomographic reconstruction. The subject is rapidly developing and in the near future is likely to deal with high-level applications like object recognition. Fascinating experiments by Y. CHOW, U. GRENANDER and D.M. KEENAN (1987), (1990) strongly support this belief.


Image Analysis, Random Fields and Markov Chain Monte Carlo Methods

Image Analysis, Random Fields and Markov Chain Monte Carlo Methods
Author: Gerhard Winkler
Publisher: Springer Science & Business Media
Total Pages: 389
Release: 2012-12-06
Genre: Mathematics
ISBN: 3642557600

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"This book is concerned with a probabilistic approach for image analysis, mostly from the Bayesian point of view, and the important Markov chain Monte Carlo methods commonly used....This book will be useful, especially to researchers with a strong background in probability and an interest in image analysis. The author has presented the theory with rigor...he doesn’t neglect applications, providing numerous examples of applications to illustrate the theory." -- MATHEMATICAL REVIEWS


Markov Random Field Modeling in Image Analysis

Markov Random Field Modeling in Image Analysis
Author: S. Z. Li
Publisher: Springer Science & Business Media
Total Pages: 0
Release: 2001
Genre: Computer science
ISBN: 9784431703099

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This updated edition includes the important progress made in Markov modeling in image analysis in recent years, such as Markov modeling of images with "macro" patterns (the FRAME model, for one), Markov chain Monte Carlo (MCMC) methods, and reversible jump MCMC."--Jacket.


Stochastic Models, Statistical Methods, and Algorithms in Image Analysis

Stochastic Models, Statistical Methods, and Algorithms in Image Analysis
Author: Piero Barone
Publisher: Springer Science & Business Media
Total Pages: 266
Release: 2012-12-06
Genre: Mathematics
ISBN: 1461229200

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This volume comprises a collection of papers by world- renowned experts on image analysis. The papers range from survey articles to research papers, and from theoretical topics such as simulated annealing through to applied image reconstruction. It covers applications as diverse as biomedicine, astronomy, and geophysics. As a result, any researcher working on image analysis will find this book provides an up-to-date overview of the field and in addition, the extensive bibliographies will make this a useful reference.


Image Analysis and Recognition

Image Analysis and Recognition
Author: Aurélio Campilho
Publisher: Springer Science & Business Media
Total Pages: 905
Release: 2004-09-24
Genre: Computers
ISBN: 3540232230

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ICIAR 2004, the International Conference on Image Analysis and Recognition, was the ?rst ICIAR conference, and was held in Porto, Portugal. ICIAR will be organized annually, and will alternate between Europe and North America. ICIAR 2005 will take place in Toronto, Ontario, Canada. The idea of o?ering these conferences came as a result of discussion between researchers in Portugal and Canada to encourage collaboration and exchange, mainly between these two countries, but also with the open participation of other countries, addressing recent advances in theory, methodology and applications. The response to the call for papers for ICIAR 2004 was very positive. From 316 full papers submitted, 210 were accepted (97 oral presentations, and 113 - sters). The review process was carried out by the Program Committee members and other reviewers; all are experts in various image analysis and recognition areas. Each paper was reviewed by at least two reviewing parties. The high q- lity of the papers in these proceedings is attributed ?rst to the authors, and second to the quality of the reviews provided by the experts. We would like to thank the authors for responding to our call, and we wholeheartedly thank the reviewers for their excellent work in such a short amount of time. We are espe- ally indebted to the Program Committee for their e?orts that allowed us to set up this publication. We were very pleased to be able to include in the conference, Prof. Murat KuntfromtheSwissFederalInstituteofTechnology,andProf. Mario ́ Figueiredo, oftheInstitutoSuperiorT ́ ecnico,inPortugal.


Monte Carlo Statistical Methods

Monte Carlo Statistical Methods
Author: Christian Robert
Publisher: Springer Science & Business Media
Total Pages: 670
Release: 2013-03-14
Genre: Mathematics
ISBN: 1475741456

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We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.


Monte Carlo Methods

Monte Carlo Methods
Author: Adrian Barbu
Publisher: Springer Nature
Total Pages: 433
Release: 2020-02-24
Genre: Mathematics
ISBN: 9811329710

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This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.


Combinatorial Image Analysis

Combinatorial Image Analysis
Author: Reinhard Klette
Publisher: Springer Science & Business Media
Total Pages: 771
Release: 2004-11-22
Genre: Computers
ISBN: 3540239421

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This volume presents the proceedings of the 10th International Workshop on Combinatorial Image Analysis, held December 1–3, 2004, in Auckland, New Zealand. Prior meetings took place in Paris (France, 1991), Ube (Japan, 1992), Washington DC (USA, 1994), Lyon (France, 1995), Hiroshima (Japan, 1997), Madras (India, 1999), Caen (France, 2000), Philadelphia (USA, 2001), and - lermo (Italy, 2003). For this workshop we received 86 submitted papers from 23 countries. Each paper was evaluated by at least two independent referees. We selected 55 papers for the conference. Three invited lectures by Vladimir Kovalevsky (Berlin), Akira Nakamura (Hiroshima), and Maurice Nivat (Paris) completed the program. Conference papers are presented in this volume under the following topical part titles: discrete tomography (3 papers), combinatorics and computational models (6), combinatorial algorithms (6), combinatorial mathematics (4), d- ital topology (7), digital geometry (7), approximation of digital sets by curves and surfaces (5), algebraic approaches (5), fuzzy image analysis (2), image s- mentation (6), and matching and recognition (7). These subjects are dealt with in the context of digital image analysis or computer vision.


Stochastic Modeling for Medical Image Analysis

Stochastic Modeling for Medical Image Analysis
Author: Ayman El-Baz
Publisher: CRC Press
Total Pages: 299
Release: 2015-11-18
Genre: Medical
ISBN: 1466599081

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Stochastic Modeling for Medical Image Analysis provides a brief introduction to medical imaging, stochastic modeling, and model-guided image analysis.Today, image-guided computer-assisted diagnostics (CAD) faces two basic challenging problems. The first is the computationally feasible and accurate modeling of images from different modalities to obt