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Stochastic Image Processing

2004-03-31 Computers
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Release : 2004-03-31
Genre : Computers
Kind : eBook
Book Rating : 925/5 ( reviews)

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Book Synopsis Stochastic Image Processing by : Chee Sun Won

Download or read book Stochastic Image Processing written by Chee Sun Won. This book was released on 2004-03-31. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Image Processing provides the first thorough treatment of Markov and hidden Markov random fields and their application to image processing. Although promoted as a promising approach for over thirty years, it has only been in the past few years that the theory and algorithms have developed to the point of providing useful solutions to old and new problems in image processing. Markov random fields are a multidimensional extension of Markov chains, but the generalization is complicated by the lack of a natural ordering of pixels in multidimensional spaces. Hidden Markov fields are a natural generalization of the hidden Markov models that have proved essential to the development of modern speech recognition, but again the multidimensional nature of the signals makes them inherently more complicated to handle. This added complexity contributed to the long time required for the development of successful methods and applications. This book collects together a variety of successful approaches to a complete and useful characterization of multidimensional Markov and hidden Markov models along with applications to image analysis. The book provides a survey and comparative development of an exciting and rapidly evolving field of multidimensional Markov and hidden Markov random fields with extensive references to the literature.

Image Processing and Analysis

2005-09-01 Computers
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Release : 2005-09-01
Genre : Computers
Kind : eBook
Book Rating : 89X/5 ( reviews)

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Book Synopsis Image Processing and Analysis by : Tony F. Chan

Download or read book Image Processing and Analysis written by Tony F. Chan. This book was released on 2005-09-01. Available in PDF, EPUB and Kindle. Book excerpt: This book develops the mathematical foundation of modern image processing and low-level computer vision, bridging contemporary mathematics with state-of-the-art methodologies in modern image processing, whilst organizing contemporary literature into a coherent and logical structure. The authors have integrated the diversity of modern image processing approaches by revealing the few common threads that connect them to Fourier and spectral analysis, the machinery that image processing has been traditionally built on. The text is systematic and well organized: the geometric, functional, and atomic structures of images are investigated, before moving to a rigorous development and analysis of several image processors. The book is comprehensive and integrative, covering the four most powerful classes of mathematical tools in contemporary image analysis and processing while exploring their intrinsic connections and integration. The material is balanced in theory and computation, following a solid theoretical analysis of model building and performance with computational implementation and numerical examples.

Stochastic Image Processing

2013-11-27 Computers
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Release : 2013-11-27
Genre : Computers
Kind : eBook
Book Rating : 572/5 ( reviews)

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Book Synopsis Stochastic Image Processing by : Chee Sun Won

Download or read book Stochastic Image Processing written by Chee Sun Won. This book was released on 2013-11-27. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Image Processing provides the first thorough treatment of Markov and hidden Markov random fields and their application to image processing. Although promoted as a promising approach for over thirty years, it has only been in the past few years that the theory and algorithms have developed to the point of providing useful solutions to old and new problems in image processing. Markov random fields are a multidimensional extension of Markov chains, but the generalization is complicated by the lack of a natural ordering of pixels in multidimensional spaces. Hidden Markov fields are a natural generalization of the hidden Markov models that have proved essential to the development of modern speech recognition, but again the multidimensional nature of the signals makes them inherently more complicated to handle. This added complexity contributed to the long time required for the development of successful methods and applications. This book collects together a variety of successful approaches to a complete and useful characterization of multidimensional Markov and hidden Markov models along with applications to image analysis. The book provides a survey and comparative development of an exciting and rapidly evolving field of multidimensional Markov and hidden Markov random fields with extensive references to the literature.

Markov Random Field Modeling in Image Analysis

2009-04-03 Computers
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Release : 2009-04-03
Genre : Computers
Kind : eBook
Book Rating : 793/5 ( reviews)

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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. This book was released on 2009-04-03. 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.

Modelling and Application of Stochastic Processes

1986-10-31 Science
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Release : 1986-10-31
Genre : Science
Kind : eBook
Book Rating : 771/5 ( reviews)

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Book Synopsis Modelling and Application of Stochastic Processes by : Uday B. Desai

Download or read book Modelling and Application of Stochastic Processes written by Uday B. Desai. This book was released on 1986-10-31. Available in PDF, EPUB and Kindle. Book excerpt: The subject of modelling and application of stochastic processes is too vast to be exhausted in a single volume. In this book, attention is focused on a small subset of this vast subject. The primary emphasis is on realization and approximation of stochastic systems. Recently there has been considerable interest in the stochastic realization problem, and hence, an attempt has been made here to collect in one place some of the more recent approaches and algorithms for solving the stochastic realiza tion problem. Various different approaches for realizing linear minimum-phase systems, linear nonminimum-phase systems, and bilinear systems are presented. These approaches range from time-domain methods to spectral-domain methods. An overview of the chapter contents briefly describes these approaches. Also, in most of these chapters special attention is given to the problem of developing numerically ef ficient algorithms for obtaining reduced-order (approximate) stochastic realizations. On the application side, chapters on use of Markov random fields for modelling and analyzing image signals, use of complementary models for the smoothing problem with missing data, and nonlinear estimation are included. Chapter 1 by Klein and Dickinson develops the nested orthogonal state space realization for ARMA processes. As suggested by the name, nested orthogonal realizations possess two key properties; (i) the state variables are orthogonal, and (ii) the system matrices for the (n + l)st order realization contain as their "upper" n-th order blocks the system matrices from the n-th order realization (nesting property).

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