Parameter Estimation Of Random Fields

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Markov Random Field Modeling in Image Analysis

Author : Stan Z. Li
Publisher : Springer Science & Business Media
ISBN 13 : 1848002793
Total Pages : 372 pages
Book Rating : 4.8/5 (48 download)

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Book Synopsis Markov Random Field Modeling in Image Analysis by : Stan Z. Li

Download or read book Markov Random Field Modeling in Image Analysis written by Stan Z. Li and published by Springer Science & Business Media. This book was released on 2009-04-03 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.

An Introduction to Conditional Random Fields

Author : Charles Sutton
Publisher : Now Pub
ISBN 13 : 9781601985729
Total Pages : 120 pages
Book Rating : 4.9/5 (857 download)

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Book Synopsis An Introduction to Conditional Random Fields by : Charles Sutton

Download or read book An Introduction to Conditional Random Fields written by Charles Sutton and published by Now Pub. This book was released on 2012 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Conditional Random Fields provides a comprehensive tutorial aimed at application-oriented practitioners seeking to apply CRFs. The monograph does not assume previous knowledge of graphical modeling, and so is intended to be useful to practitioners in a wide variety of fields.

Markov Random Fields in Image Segmentation

Author : Zoltan Kato
Publisher : Now Pub
ISBN 13 : 9781601985880
Total Pages : 168 pages
Book Rating : 4.9/5 (858 download)

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Book Synopsis Markov Random Fields in Image Segmentation by : Zoltan Kato

Download or read book Markov Random Fields in Image Segmentation written by Zoltan Kato and published by Now Pub. This book was released on 2012-09 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Random Fields in Image Segmentation provides 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 formulated within an image labeling framework, 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. 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. Classical optimization algorithms including simulated annealing and deterministic relaxation are treated along with more recent graph cut-based algorithms. The primary goal of this monograph is to demonstrate the basic steps to construct an easily applicable MRF segmentation model and further develop its multi-scale and hierarchical implementations as well as their combination in a multilayer model. Representative examples from remote sensing and biological imaging are analyzed in full detail to illustrate the applicability of these MRF models. Furthermore, a sample implementation of the most important segmentation algorithms is available as supplementary software. Markov Random Fields in Image Segmentation is an invaluable resource for every student, engineer, or researcher dealing with Markovian modeling for image segmentation.

Markov Random Field Modeling in Computer Vision

Author : S.Z. Li
Publisher : Springer Science & Business Media
ISBN 13 : 4431669337
Total Pages : 274 pages
Book Rating : 4.4/5 (316 download)

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Book Synopsis Markov Random Field Modeling in Computer Vision by : S.Z. Li

Download or read book Markov Random Field Modeling in Computer Vision written by S.Z. Li and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov random field (MRF) modeling provides a basis for the characterization of contextual constraints on visual interpretation and enables us to develop optimal vision algorithms systematically based on sound principles. This book presents a comprehensive study on using MRFs to solve computer vision problems, covering the following parts essential to the subject: introduction to fundamental theories, formulations of various vision models in the MRF framework, MRF parameter estimation, and optimization algorithms. Various MRF vision models are presented in a unified form, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This book is an excellent reference for researchers working in computer vision, image processing, pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in the subject.

Random Fields and Geometry

Author : R. J. Adler
Publisher : Springer Science & Business Media
ISBN 13 : 0387481168
Total Pages : 455 pages
Book Rating : 4.3/5 (874 download)

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Book Synopsis Random Fields and Geometry by : R. J. Adler

Download or read book Random Fields and Geometry written by R. J. Adler and published by Springer Science & Business Media. This book was released on 2009-01-29 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is devoted to a completely new approach to geometric problems arising in the study of random fields. The groundbreaking material in Part III, for which the background is carefully prepared in Parts I and II, is of both theoretical and practical importance, and striking in the way in which problems arising in geometry and probability are beautifully intertwined. "Random Fields and Geometry" will be useful for probabilists and statisticians, and for theoretical and applied mathematicians who wish to learn about new relationships between geometry and probability. It will be helpful for graduate students in a classroom setting, or for self-study. Finally, this text will serve as a basic reference for all those interested in the companion volume of the applications of the theory.

Model Calibration and Parameter Estimation

Author : Ne-Zheng Sun
Publisher : Springer
ISBN 13 : 1493923234
Total Pages : 638 pages
Book Rating : 4.4/5 (939 download)

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Book Synopsis Model Calibration and Parameter Estimation by : Ne-Zheng Sun

Download or read book Model Calibration and Parameter Estimation written by Ne-Zheng Sun and published by Springer. This book was released on 2015-07-01 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-part book provides a comprehensive and systematic introduction to these challenging topics such as model calibration, parameter estimation, reliability assessment, and data collection design. Part 1 covers the classical inverse problem for parameter estimation in both deterministic and statistical frameworks, Part 2 is dedicated to system identification, hyperparameter estimation, and model dimension reduction, and Part 3 considers how to collect data and construct reliable models for prediction and decision-making. For the first time, topics such as multiscale inversion, stochastic field parameterization, level set method, machine learning, global sensitivity analysis, data assimilation, model uncertainty quantification, robust design, and goal-oriented modeling, are systematically described and summarized in a single book from the perspective of model inversion, and elucidated with numerical examples from environmental and water resources modeling. Readers of this book will not only learn basic concepts and methods for simple parameter estimation, but also get familiar with advanced methods for modeling complex systems. Algorithms for mathematical tools used in this book, such as numerical optimization, automatic differentiation, adaptive parameterization, hierarchical Bayesian, metamodeling, Markov chain Monte Carlo, are covered in details. This book can be used as a reference for graduate and upper level undergraduate students majoring in environmental engineering, hydrology, and geosciences. It also serves as an essential reference book for professionals such as petroleum engineers, mining engineers, chemists, mechanical engineers, biologists, biology and medical engineering, applied mathematicians, and others who perform mathematical modeling.

Hybrid Random Fields

Author : Antonino Freno
Publisher : Springer Science & Business Media
ISBN 13 : 3642203086
Total Pages : 217 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Hybrid Random Fields by : Antonino Freno

Download or read book Hybrid Random Fields written by Antonino Freno and published by Springer Science & Business Media. This book was released on 2011-04-11 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives. -- Manfred Jaeger, Aalborg Universitet The book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [...] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it. -- Marco Gori, Università degli Studi di Siena Graphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data.

Image Analysis, Random Fields and Dynamic Monte Carlo Methods

Author : Gerhard Winkler
Publisher : Springer Science & Business Media
ISBN 13 : 3642975224
Total Pages : 321 pages
Book Rating : 4.6/5 (429 download)

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Book Synopsis Image Analysis, Random Fields and Dynamic Monte Carlo Methods by : Gerhard Winkler

Download or read book Image Analysis, Random Fields and Dynamic Monte Carlo Methods written by Gerhard Winkler and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Random Fields on a Network

Author : Xavier Guyon
Publisher : Springer Science & Business Media
ISBN 13 : 9780387944289
Total Pages : 294 pages
Book Rating : 4.9/5 (442 download)

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Book Synopsis Random Fields on a Network by : Xavier Guyon

Download or read book Random Fields on a Network written by Xavier Guyon and published by Springer Science & Business Media. This book was released on 1995-06-23 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theory of spatial models over lattices, or random fields as they are known, has developed significantly over recent years. This book provides a graduate-level introduction to the subject which assumes only a basic knowledge of probability and statistics, finite Markov chains, and the spectral theory of second-order processes. A particular strength of this book is its emphasis on examples - both to motivate the theory which is being developed, and to demonstrate the applications which range from statistical mechanics to image analysis and from statistics to stochastic algorithms.

Random Fields for Spatial Data Modeling

Author : Dionissios T. Hristopulos
Publisher : Springer Nature
ISBN 13 : 9402419187
Total Pages : 884 pages
Book Rating : 4.4/5 (24 download)

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Book Synopsis Random Fields for Spatial Data Modeling by : Dionissios T. Hristopulos

Download or read book Random Fields for Spatial Data Modeling written by Dionissios T. Hristopulos and published by Springer Nature. This book was released on 2020-02-17 with total page 884 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an inter-disciplinary introduction to the theory of random fields and its applications. Spatial models and spatial data analysis are integral parts of many scientific and engineering disciplines. Random fields provide a general theoretical framework for the development of spatial models and their applications in data analysis. The contents of the book include topics from classical statistics and random field theory (regression models, Gaussian random fields, stationarity, correlation functions) spatial statistics (variogram estimation, model inference, kriging-based prediction) and statistical physics (fractals, Ising model, simulated annealing, maximum entropy, functional integral representations, perturbation and variational methods). The book also explores links between random fields, Gaussian processes and neural networks used in machine learning. Connections with applied mathematics are highlighted by means of models based on stochastic partial differential equations. An interlude on autoregressive time series provides useful lower-dimensional analogies and a connection with the classical linear harmonic oscillator. Other chapters focus on non-Gaussian random fields and stochastic simulation methods. The book also presents results based on the author’s research on Spartan random fields that were inspired by statistical field theories originating in physics. The equivalence of the one-dimensional Spartan random field model with the classical, linear, damped harmonic oscillator driven by white noise is highlighted. Ideas with potentially significant computational gains for the processing of big spatial data are presented and discussed. The final chapter concludes with a description of the Karhunen-Loève expansion of the Spartan model. The book will appeal to engineers, physicists, and geoscientists whose research involves spatial models or spatial data analysis. Anyone with background in probability and statistics can read at least parts of the book. Some chapters will be easier to understand by readers familiar with differential equations and Fourier transforms.

Statistical Analysis of Random Fields

Author : A.A. Ivanov
Publisher : Springer Science & Business Media
ISBN 13 : 9400911831
Total Pages : 255 pages
Book Rating : 4.4/5 (9 download)

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Book Synopsis Statistical Analysis of Random Fields by : A.A. Ivanov

Download or read book Statistical Analysis of Random Fields written by A.A. Ivanov and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'Et moi ... - si j'avait su comment en revcnir. One service mathematics has rendered the je n'y scrais point aile.' human race. It has put common sense back where it belongs, on the topmost shclf next Jules Verne to the dusty canister labdlcd 'discarded non· The series is divergent; therefore we may be sense'. able to do something with it Eric T. Bell O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics .. .'; 'One service logic has rendered com puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.

Image Analysis, Random Fields and Markov Chain Monte Carlo Methods

Author : Gerhard Winkler
Publisher : Springer Science & Business Media
ISBN 13 : 3642557600
Total Pages : 389 pages
Book Rating : 4.6/5 (425 download)

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Book Synopsis Image Analysis, Random Fields and Markov Chain Monte Carlo Methods by : Gerhard Winkler

Download or read book Image Analysis, Random Fields and Markov Chain Monte Carlo Methods written by Gerhard Winkler and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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

Gaussian and Non-Gaussian Linear Time Series and Random Fields

Author : Murray Rosenblatt
Publisher : Springer Science & Business Media
ISBN 13 : 1461212626
Total Pages : 252 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Gaussian and Non-Gaussian Linear Time Series and Random Fields by : Murray Rosenblatt

Download or read book Gaussian and Non-Gaussian Linear Time Series and Random Fields written by Murray Rosenblatt and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: The principal focus here is on autoregressive moving average models and analogous random fields, with probabilistic and statistical questions also being discussed. The book contrasts Gaussian models with noncausal or noninvertible (nonminimum phase) non-Gaussian models and deals with problems of prediction and estimation. New results for nonminimum phase non-Gaussian processes are exposited and open questions are noted. Intended as a text for gradutes in statistics, mathematics, engineering, the natural sciences and economics, the only recommendation is an initial background in probability theory and statistics. Notes on background, history and open problems are given at the end of the book.

Markov Random Fields for Vision and Image Processing

Author : Andrew Blake
Publisher : MIT Press
ISBN 13 : 0262015773
Total Pages : 472 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Markov Random Fields for Vision and Image Processing by : Andrew Blake

Download or read book Markov Random Fields for Vision and Image Processing written by Andrew Blake and published by MIT Press. This book was released on 2011-07-22 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-the-art research on MRFs, successful MRF applications, and advanced topics for future study. This volume demonstrates the power of the Markov random field (MRF) in vision, treating the MRF both as a tool for modeling image data and, utilizing recently developed algorithms, as a means of making inferences about images. These inferences concern underlying image and scene structure as well as solutions to such problems as image reconstruction, image segmentation, 3D vision, and object labeling. It offers key findings and state-of-the-art research on both algorithms and applications. After an introduction to the fundamental concepts used in MRFs, the book reviews some of the main algorithms for performing inference with MRFs; presents successful applications of MRFs, including segmentation, super-resolution, and image restoration, along with a comparison of various optimization methods; discusses advanced algorithmic topics; addresses limitations of the strong locality assumptions in the MRFs discussed in earlier chapters; and showcases applications that use MRFs in more complex ways, as components in bigger systems or with multiterm energy functions. The book will be an essential guide to current research on these powerful mathematical tools.

The Geometry of Random Fields

Author : Robert J. Adler
Publisher : SIAM
ISBN 13 : 0898716934
Total Pages : 295 pages
Book Rating : 4.8/5 (987 download)

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Book Synopsis The Geometry of Random Fields by : Robert J. Adler

Download or read book The Geometry of Random Fields written by Robert J. Adler and published by SIAM. This book was released on 2010-01-28 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: An important treatment of the geometric properties of sets generated by random fields, including a comprehensive treatment of the mathematical basics of random fields in general. It is a standard reference for all researchers with an interest in random fields, whether they be theoreticians or come from applied areas.

Parameter Estimation for Scientists and Engineers

Author : Adriaan van den Bos
Publisher : Wiley-Interscience
ISBN 13 :
Total Pages : 296 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Parameter Estimation for Scientists and Engineers by : Adriaan van den Bos

Download or read book Parameter Estimation for Scientists and Engineers written by Adriaan van den Bos and published by Wiley-Interscience. This book was released on 2007-07-16 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher description

Limit Theorems for Random Fields with Singular Spectrum

Author : Nicolai Leonenko
Publisher : Springer Science & Business Media
ISBN 13 : 9401146071
Total Pages : 410 pages
Book Rating : 4.4/5 (11 download)

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Book Synopsis Limit Theorems for Random Fields with Singular Spectrum by : Nicolai Leonenko

Download or read book Limit Theorems for Random Fields with Singular Spectrum written by Nicolai Leonenko and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents limit theorems for nonlinear functionals of random fields with singular spectrum on the basis of various asymptotic expansions. The first chapter treats basic concepts of the spectral theory of random fields, some important examples of random processes and fields with singular spectrum, and Tauberian and Abelian theorems for covariance function of long-memory random fields. Chapter 2 is devoted to limit theorems for spherical averages of nonlinear transformations of Gaussian and chi-square random fields. Chapter 3 summarises some limit theorems for geometric type functionals of random fields. Limit theorems for the solutions of Burgers' equation with random data via parabolic and hyperbolic rescaling are demonstrated in Chapter 4. Lastly, Chapter 5 deals with some problems for statistical analysis of random fields with singular spectrum. Audience: This book will be of interest to mathematicians who use random fields in engineering or other applications.