Stochastic Image Processing

Author : Chee Sun Won,Robert M. Gray
Publisher : Springer Science & Business Media
Page : 176 pages
File Size : 49,5 Mb
Release : 2013-11-27
Category : Computers
ISBN : 9781441988577

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Stochastic Image Processing by Chee Sun Won,Robert M. Gray Pdf

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.

Author : Tony F. Chan,Jianhong (Jackie) Shen
Publisher : SIAM
Page : 421 pages
File Size : 42,8 Mb
Release : 2005-01-01
Category : Computers
ISBN : 0898717876

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Image Processing and Analysis by Tony F. Chan,Jianhong (Jackie) Shen Pdf

At no other time in human history have the influence and impact of image processing on modern society, science, and technology been so explosive. Image processing has become a critical component in contemporary science and technology and has many important applications. This book develops the mathematical foundation of modern image processing and low-level computer vision, and presents a general framework from the analysis of image structures and patterns to their processing. The core mathematical and computational ingredients of several important image processing tasks are investigated. The book bridges contemporary mathematics with state-of-the-art methodologies in modern image processing while organizing the vast contemporary literature into a coherent and logical structure.

Author : Ayman El-Baz,Georgy Gimel’farb,Jasjit S. Suri
Publisher : CRC Press
Page : 284 pages
File Size : 43,7 Mb
Release : 2015-11-18
Category : Medical
ISBN : 9781466599086

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Stochastic Modeling for Medical Image Analysis by Ayman El-Baz,Georgy Gimel’farb,Jasjit S. Suri Pdf

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 obtain clinically useful information. The second is the accurate and fast inferring of meaningful and clinically valid CAD decisions and/or predictions on the basis of model-guided image analysis. To help address this, this book details original stochastic appearance and shape models with computationally feasible and efficient learning techniques for improving the performance of object detection, segmentation, alignment, and analysis in a number of important CAD applications. The book demonstrates accurate descriptions of visual appearances and shapes of the goal objects and their background to help solve a number of important and challenging CAD problems. The models focus on the first-order marginals of pixel/voxel-wise signals and second- or higher-order Markov-Gibbs random fields of these signals and/or labels of regions supporting the goal objects in the lattice. This valuable resource presents the latest state of the art in stochastic modeling for medical image analysis while incorporating fully tested experimental results throughout.

Author : Seetharaman K.
Publisher : Unknown
Page : 144 pages
File Size : 49,5 Mb
Release : 2014-04
Category : Electronic
ISBN : 3659532150

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Image Processing: Stochastic Model Based Approach by Seetharaman K. Pdf

Author : Xavier Descombes
Publisher : John Wiley & Sons
Page : 215 pages
File Size : 40,6 Mb
Release : 2013-05-06
Category : Technology & Engineering
ISBN : 9781118601136

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Stochastic Geometry for Image Analysis by Xavier Descombes Pdf

This book develops the stochastic geometry framework for image analysis purpose. Two main frameworks are described: marked point process and random closed sets models. We derive the main issues for defining an appropriate model. The algorithms for sampling and optimizing the models as well as for estimating parameters are reviewed. Numerous applications, covering remote sensing images, biological and medical imaging, are detailed. This book provides all the necessary tools for developing an image analysis application based on modern stochastic modeling.

Author : Kayvan Najarian,Robert Splinter
Publisher : CRC Press
Page : 466 pages
File Size : 42,9 Mb
Release : 2005-12-21
Category : Medical
ISBN : 0849320992

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Biomedical Signal and Image Processing by Kayvan Najarian,Robert Splinter Pdf

All of the biomedical measurement technologies, which are now instrumental to the medical field, are essentially useless without proper signal and image processing. Biomedical Signal and Image Processing is unique in providing a comprehensive survey of all the conventional and advanced imaging modalities and the main computational methods used for processing the data obtained from each. This book offers self-contained coverage of the mathematics and biology/physiology necessary to build effective algorithms and programs for biomedical signal and image processing applications. The first part of the book details the main signal and image processing, pattern recognition, and feature extraction techniques along with computational methods from other fields such as information theory and stochastic processes. Building on this foundation, the second part explores the major one-dimensional biological signals, the biological origin and importance of each signal, and the commonly used processing techniques with an emphasis on physiology and diagnostic applications, while the third section does the same for imaging modalities. Throughout the book, the authors rely on practical examples using real data from biomedical systems. They supply several programming examples in MATLAB® to provide hands-on experience and insight Integrating all major modalities and computational techniques in a single source, Biomedical Signal and Image Processing is a perfect introduction to the field as well as an ideal reference for the established professional.

Author : Jiri Jan
Publisher : CRC Press
Page : 619 pages
File Size : 42,7 Mb
Release : 2019-08-30
Category : Medical
ISBN : 9781351387903

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Medical Image Processing, Reconstruction and Analysis by Jiri Jan Pdf

Differently oriented specialists and students involved in image processing and analysis need to have a firm grasp of concepts and methods used in this now widely utilized area. This book aims at being a single-source reference providing such foundations in the form of theoretical yet clear and easy to follow explanations of underlying generic concepts. Medical Image Processing, Reconstruction and Analysis – Concepts and Methods explains the general principles and methods of image processing and analysis, focusing namely on applications used in medical imaging. The content of this book is divided into three parts: Part I – Images as Multidimensional Signals provides the introduction to basic image processing theory, explaining it for both analogue and digital image representations. Part II – Imaging Systems as Data Sources offers a non-traditional view on imaging modalities, explaining their principles influencing properties of the obtained images that are to be subsequently processed by methods described in this book. Newly, principles of novel modalities, as spectral CT, functional MRI, ultrafast planar-wave ultrasonography and optical coherence tomography are included. Part III – Image Processing and Analysis focuses on tomographic image reconstruction, image fusion and methods of image enhancement and restoration; further it explains concepts of low-level image analysis as texture analysis, image segmentation and morphological transforms. A new chapter deals with selected areas of higher-level analysis, as principal and independent component analysis and particularly the novel analytic approach based on deep learning. Briefly, also the medical image-processing environment is treated, including processes for image archiving and communication. Features Presents a theoretically exact yet understandable explanation of image processing and analysis concepts and methods Offers practical interpretations of all theoretical conclusions, as derived in the consistent explanation Provides a concise treatment of a wide variety of medical imaging modalities including novel ones, with respect to properties of provided image data

Author : Giovanni Peccati,Matthias Reitzner
Publisher : Springer
Page : 346 pages
File Size : 50,9 Mb
Release : 2016-07-07
Category : Mathematics
ISBN : 9783319052335

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Stochastic Analysis for Poisson Point Processes by Giovanni Peccati,Matthias Reitzner Pdf

Stochastic geometry is the branch of mathematics that studies geometric structures associated with random configurations, such as random graphs, tilings and mosaics. Due to its close ties with stereology and spatial statistics, the results in this area are relevant for a large number of important applications, e.g. to the mathematical modeling and statistical analysis of telecommunication networks, geostatistics and image analysis. In recent years – due mainly to the impetus of the authors and their collaborators – a powerful connection has been established between stochastic geometry and the Malliavin calculus of variations, which is a collection of probabilistic techniques based on the properties of infinite-dimensional differential operators. This has led in particular to the discovery of a large number of new quantitative limit theorems for high-dimensional geometric objects. This unique book presents an organic collection of authoritative surveys written by the principal actors in this rapidly evolving field, offering a rigorous yet lively presentation of its many facets.

Author : Piero Barone,Arnoldo Frigessi,Mauro Piccioni
Publisher : Springer Science & Business Media
Page : 266 pages
File Size : 53,9 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461229209

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Stochastic Models, Statistical Methods, and Algorithms in Image Analysis by Piero Barone,Arnoldo Frigessi,Mauro Piccioni Pdf

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.

Author : Uday B. Desai
Publisher : Springer Science & Business Media
Page : 310 pages
File Size : 55,7 Mb
Release : 1986-10-31
Category : Science
ISBN : 0898381770

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Modelling and Application of Stochastic Processes by Uday B. Desai Pdf

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).

Author : David Insua,Fabrizio Ruggeri,Mike Wiper
Publisher : John Wiley & Sons
Page : 315 pages
File Size : 45,5 Mb
Release : 2012-04-02
Category : Mathematics
ISBN : 9781118304037

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Bayesian Analysis of Stochastic Process Models by David Insua,Fabrizio Ruggeri,Mike Wiper Pdf

Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.

Author : Tobias Preusser,Robert M. Kirby,Torben Pätz
Publisher : Springer Nature
Page : 150 pages
File Size : 50,5 Mb
Release : 2022-06-01
Category : Mathematics
ISBN : 9783031025945

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Stochastic Partial Differential Equations for Computer Vision with Uncertain Data by Tobias Preusser,Robert M. Kirby,Torben Pätz Pdf

In image processing and computer vision applications such as medical or scientific image data analysis, as well as in industrial scenarios, images are used as input measurement data. It is good scientific practice that proper measurements must be equipped with error and uncertainty estimates. For many applications, not only the measured values but also their errors and uncertainties, should be—and more and more frequently are—taken into account for further processing. This error and uncertainty propagation must be done for every processing step such that the final result comes with a reliable precision estimate. The goal of this book is to introduce the reader to the recent advances from the field of uncertainty quantification and error propagation for computer vision, image processing, and image analysis that are based on partial differential equations (PDEs). It presents a concept with which error propagation and sensitivity analysis can be formulated with a set of basic operations. The approach discussed in this book has the potential for application in all areas of quantitative computer vision, image processing, and image analysis. In particular, it might help medical imaging finally become a scientific discipline that is characterized by the classical paradigms of observation, measurement, and error awareness. This book is comprised of eight chapters. After an introduction to the goals of the book (Chapter 1), we present a brief review of PDEs and their numerical treatment (Chapter 2), PDE-based image processing (Chapter 3), and the numerics of stochastic PDEs (Chapter 4). We then proceed to define the concept of stochastic images (Chapter 5), describe how to accomplish image processing and computer vision with stochastic images (Chapter 6), and demonstrate the use of these principles for accomplishing sensitivity analysis (Chapter 7). Chapter 8 concludes the book and highlights new research topics for the future.

Author : Stan Z. Li
Publisher : Springer Science & Business Media
Page : 372 pages
File Size : 54,5 Mb
Release : 2009-04-03
Category : Computers
ISBN : 9781848002791

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Markov Random Field Modeling in Image Analysis by Stan Z. Li Pdf

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.

Author : Oliver Ibe
Publisher : Newnes
Page : 515 pages
File Size : 51,5 Mb
Release : 2013-05-22
Category : Mathematics
ISBN : 9780124078390

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Markov Processes for Stochastic Modeling by Oliver Ibe Pdf

Markov processes are processes that have limited memory. In particular, their dependence on the past is only through the previous state. They are used to model the behavior of many systems including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems. Covering a wide range of areas of application of Markov processes, this second edition is revised to highlight the most important aspects as well as the most recent trends and applications of Markov processes. The author spent over 16 years in the industry before returning to academia, and he has applied many of the principles covered in this book in multiple research projects. Therefore, this is an applications-oriented book that also includes enough theory to provide a solid ground in the subject for the reader. Presents both the theory and applications of the different aspects of Markov processes Includes numerous solved examples as well as detailed diagrams that make it easier to understand the principle being presented Discusses different applications of hidden Markov models, such as DNA sequence analysis and speech analysis.

Author : Anonim
Publisher : Unknown
Page : 288 pages
File Size : 44,8 Mb
Release : 1994
Category : Image processing
ISBN : UOM:39015028287087

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Neural and Stochastic Methods in Image and Signal Processing by Anonim Pdf