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: 412
Release: 2003
Genre: Computers
ISBN: 9783540442134

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CD-ROM (version a 1.01) includes: software "AntsInFields", graphical user interfaces, an an educational, self explaining, and self contained library of living documents.--Intro. p. 5.

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

Spectral Models of Random Fields in Monte Carlo Methods

Spectral Models of Random Fields in Monte Carlo Methods
Author: Serge M. Prigarin
Publisher: VSP
Total Pages: 220
Release: 2001
Genre: Science
ISBN: 9789067643436

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Spectral models were developed in the 1970s and have appeared to be very promising for various applications. Nowadays, spectral models are extensively used for stochastic simulation in atmosphere and ocean optics, turbulence theory, analysis of pollution transport for porous media, astrophysics, and other fields of science. The spectral models presented in this monograph represent a new class of numerical methods aimed at simulation of random processes and fields. The book is divided into four chapters, which deal with scalar spectral models and some of their applications, vector-valued spectral models, convergence of spectral models, and problems of optimisation and convergence for functional Monte Carlo methods. Furthermore, the monograph includes four appendices, in which auxiliary information is presented and additional problems are discussed. The book will be of value and interest to experts in Monte Carlo methods, as well as to those interested in the theory and applications of stochastic simulation.

Image Textures and Gibbs Random Fields

Image Textures and Gibbs Random Fields
Author: Georgy L. Gimel'farb
Publisher: Springer Science & Business Media
Total Pages: 263
Release: 2012-12-06
Genre: Computers
ISBN: 9401144613

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Image analysis is one of the most challenging areas in today's computer sci ence, and image technologies are used in a host of applications. This book concentrates on image textures and presents novel techniques for their sim ulation, retrieval, and segmentation using specific Gibbs random fields with multiple pairwise interaction between signals as probabilistic image models. These models and techniques were developed mainly during the previous five years (in relation to April 1999 when these words were written). While scanning these pages you may notice that, in spite of long equa tions, the mathematical background is extremely simple. I have tried to avoid complex abstract constructions and give explicit physical (to be spe cific, "image-based") explanations to all the mathematical notions involved. Therefore it is hoped that the book can be easily read both by professionals and graduate students in computer science and electrical engineering who take an interest in image analysis and synthesis. Perhaps, mathematicians studying applications of random fields may find here some less traditional, and thus controversial, views and techniques.

Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data

Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data
Author: Pramod K. Varshney
Publisher: Springer Science & Business Media
Total Pages: 344
Release: 2013-03-09
Genre: Science
ISBN: 3662056054

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The first of its kind, this book reviews image processing tools and techniques including Independent Component Analysis, Mutual Information, Markov Random Field Models and Support Vector Machines. The book also explores a number of experimental examples based on a variety of remote sensors. The book will be useful to people involved in hyperspectral imaging research, as well as by remote-sensing data like geologists, hydrologists, environmental scientists, civil engineers and computer scientists.

Academic Press Library in Signal Processing

Academic Press Library in Signal Processing
Author:
Publisher: Academic Press
Total Pages: 1131
Release: 2013-09-14
Genre: Technology & Engineering
ISBN: 0123972256

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This fourth volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in Image, Video Processing and Analysis, Hardware, Audio, Acoustic and Speech Processing. With this reference source you will: Quickly grasp a new area of research Understand the underlying principles of a topic and its application Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved Quick tutorial reviews of important and emerging topics of research in Image, Video Processing and Analysis, Hardware, Audio, Acoustic and Speech Processing Presents core principles and shows their application Reference content on core principles, technologies, algorithms and applications Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic

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.