Probabilistic Graphical Models For Computer Vision
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Probabilistic Graphical Models
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Author : Luis Enrique Sucar
language : en
Publisher: Springer
Release Date : 2015-06-19
Probabilistic Graphical Models written by Luis Enrique Sucar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-19 with Computers categories.
This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Features: presents a unified framework encompassing all of the main classes of PGMs; describes the practical application of the different techniques; examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter.
Probabilistic Graphical Models For Computer Vision
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Author : Qiang Ji
language : en
Publisher: Academic Press
Release Date : 2019-12-12
Probabilistic Graphical Models For Computer Vision written by Qiang Ji and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-12 with Technology & Engineering categories.
Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and teaches how to develop the PGM model from training data. This book discusses PGMs and their significance in the context of solving computer vision problems, giving the basic concepts, definitions and properties. It also provides a comprehensive introduction to well-established theories for different types of PGMs, including both directed and undirected PGMs, such as Bayesian Networks, Markov Networks and their variants. - Discusses PGM theories and techniques with computer vision examples - Focuses on well-established PGM theories that are accompanied by corresponding pseudocode for computer vision - Includes an extensive list of references, online resources and a list of publicly available and commercial software - Covers computer vision tasks, including feature extraction and image segmentation, object and facial recognition, human activity recognition, object tracking and 3D reconstruction
Probabilistic Graphical Models
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Author : Daphne Koller
language : en
Publisher: MIT Press
Release Date : 2009-07-31
Probabilistic Graphical Models written by Daphne Koller and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-31 with Computers categories.
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
Computer Vision
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Author : Simon J. D. Prince
language : en
Publisher: Cambridge University Press
Release Date : 2012-06-18
Computer Vision written by Simon J. D. Prince and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-18 with Computers categories.
A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.
Structured Learning And Prediction In Computer Vision
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Author : Sebastian Nowozin
language : en
Publisher: Now Publishers Inc
Release Date : 2011
Structured Learning And Prediction In Computer Vision written by Sebastian Nowozin and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision.
Handbook Of Graphical Models
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Author : Marloes Maathuis
language : en
Publisher: CRC Press
Release Date : 2018-11-12
Handbook Of Graphical Models written by Marloes Maathuis and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-12 with Mathematics categories.
A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models facilitate tractable computation with multivariate distributions, making the models a valuable tool with a plethora of applications. Furthermore, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference. While there exist a number of excellent books on graphical models, the field has grown so much that individual authors can hardly cover its entire scope. Moreover, the field is interdisciplinary by nature. Through chapters by leading researchers from different areas, this handbook provides a broad and accessible overview of the state of the art. Key features: * Contributions by leading researchers from a range of disciplines * Structured in five parts, covering foundations, computational aspects, statistical inference, causal inference, and applications * Balanced coverage of concepts, theory, methods, examples, and applications * Chapters can be read mostly independently, while cross-references highlight connections The handbook is targeted at a wide audience, including graduate students, applied researchers, and experts in graphical models.
Bayesian Reasoning And Machine Learning
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Author : David Barber
language : en
Publisher: Cambridge University Press
Release Date : 2012-02-02
Bayesian Reasoning And Machine Learning written by David Barber and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-02 with Computers categories.
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
Emerging Topics In Computer Vision And Its Applications
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Author : C. H. Chen
language : en
Publisher: World Scientific
Release Date : 2012
Emerging Topics In Computer Vision And Its Applications written by C. H. Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
This book gives a comprehensive overview of the most advanced theories, methodologies and applications in computer vision. Particularly, it gives an extensive coverage of 3D and robotic vision problems. Example chapters featured are Fourier methods for 3D surface modeling and analysis, use of constraints for calibration-free 3D Euclidean reconstruction, novel photogeometric methods for capturing static and dynamic objects, performance evaluation of robot localization methods in outdoor terrains, integrating 3D vision with force/tactile sensors, tracking via in-floor sensing, self-calibration of camera networks, etc. Some unique applications of computer vision in marine fishery, biomedical issues, driver assistance, are also highlighted.
Pattern Recognition And Machine Learning
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Author : Christopher M. Bishop
language : en
Publisher: Springer Verlag
Release Date : 2006-08-17
Pattern Recognition And Machine Learning written by Christopher M. Bishop and has been published by Springer Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-08-17 with Computers categories.
This is the first text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It provides the first text to use graphical models to describe probability distributions when there are no other books that apply graphical models to machine learning. It is also the first four-color book on pattern recognition. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher.
Machine Learning
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Author : Kevin P. Murphy
language : en
Publisher: MIT Press
Release Date : 2012-08-24
Machine Learning written by Kevin P. Murphy and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-24 with Computers categories.
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.