Probabilistic Graphical Models for Computer Vision  Book

Probabilistic Graphical Models for Computer Vision


  • Author : Qiang Ji
  • Publisher : Academic Press
  • Release Date : 2019-12-12
  • Genre: Computers
  • Pages : 294
  • ISBN 10 : 9780128034958
  • Total Read : 89
  • File Size : 20,6 Mb

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Probabilistic Graphical Models for Computer Vision Summary:

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 for Computer Vision Book

Probabilistic Graphical Models for Computer Vision


  • Author : Qiang Ji
  • Publisher : Academic Press
  • Release Date : 2019-11
  • Genre: Uncategoriezed
  • Pages : 294
  • ISBN 10 : 9780128034675
  • Total Read : 82
  • File Size : 13,6 Mb

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Probabilistic Graphical Models for Computer Vision Summary:

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 Book

Probabilistic Graphical Models


  • Author : Luis Enrique Sucar
  • Publisher : Springer Nature
  • Release Date : 2020-12-23
  • Genre: Computers
  • Pages : 355
  • ISBN 10 : 9783030619435
  • Total Read : 62
  • File Size : 7,8 Mb

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Probabilistic Graphical Models Summary:

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, graphical models, and deep learning, as well as an even greater number of exercises. 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. Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also

Probabilistic Graphical Models Book

Probabilistic Graphical Models


  • Author : Daphne Koller
  • Publisher : MIT Press
  • Release Date : 2009-07-31
  • Genre: Computers
  • Pages : 1270
  • ISBN 10 : 9780262258357
  • Total Read : 74
  • File Size : 8,5 Mb

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Probabilistic Graphical Models Summary:

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

Handbook Of Pattern Recognition And Computer Vision  2nd Edition  Book

Handbook Of Pattern Recognition And Computer Vision 2nd Edition


  • Author : Chi Hau Chen
  • Publisher : World Scientific
  • Release Date : 1999-03-12
  • Genre: Computers
  • Pages : 1044
  • ISBN 10 : 9789814497640
  • Total Read : 76
  • File Size : 18,9 Mb

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Handbook Of Pattern Recognition And Computer Vision 2nd Edition Summary:

The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference. This indispensable handbook will continue to serve as an authoritative and comprehensive guide in the field.

Handbook of Graphical Models Book

Handbook of Graphical Models


  • Author : Marloes Maathuis
  • Publisher : CRC Press
  • Release Date : 2018-11-12
  • Genre: Mathematics
  • Pages : 536
  • ISBN 10 : 9780429874246
  • Total Read : 77
  • File Size : 11,8 Mb

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Handbook of Graphical Models Summary:

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.

Learning Probabilistic Graphical Models in R Book

Learning Probabilistic Graphical Models in R


  • Author : David Bellot
  • Publisher : Packt Publishing Ltd
  • Release Date : 2016-04-29
  • Genre: Computers
  • Pages : 250
  • ISBN 10 : 9781784397418
  • Total Read : 84
  • File Size : 5,8 Mb

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Learning Probabilistic Graphical Models in R Summary:

Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R About This Book Predict and use a probabilistic graphical models (PGM) as an expert system Comprehend how your computer can learn Bayesian modeling to solve real-world problems Know how to prepare data and feed the models by using the appropriate algorithms from the appropriate R package Who This Book Is For This book is for anyone who has to deal with lots of data and draw conclusions from it, especially when the data is noisy or uncertain. Data scientists, machine learning enthusiasts, engineers, and those who curious about the latest advances in machine learning will find PGM interesting. What You Will Learn Understand the concepts of PGM and which type of PGM to use for which problem Tune the model's parameters and explore new models automatically Understand the basic principles of Bayesian models, from simple to advanced Transform the old linear regression model into a powerful probabilistic model Use standard industry models but with the power of PGM Understand the advanced models used throughout today's industry See how to compute posterior distribution with exact and approximate inference algorithms In Detail Probabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models. We'll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we'll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you'll see the advantage of going probabilistic when you want to do pre

Structured Learning and Prediction in Computer Vision Book

Structured Learning and Prediction in Computer Vision


  • Author : Sebastian Nowozin
  • Publisher : Now Publishers Inc
  • Release Date : 2011
  • Genre: Computers
  • Pages : 195
  • ISBN 10 : 9781601984562
  • Total Read : 60
  • File Size : 18,7 Mb

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Structured Learning and Prediction in Computer Vision Summary:

Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision.