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

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Probabilistic Graphical Models for Computer Vision Excerpt :

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

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Probabilistic Graphical Models for Computer Vision Excerpt :

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
  • Release Date : 2015-06-19
  • Genre: Computers
  • Pages : 253
  • ISBN 10 : 9781447166993

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Probabilistic Graphical Models Excerpt :

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 Book

Probabilistic Graphical Models


  • Author : Daphne Koller
  • Publisher : MIT Press
  • Release Date : 2009-07-31
  • Genre: Computers
  • Pages : 1270
  • ISBN 10 : 9780262258357

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Probabilistic Graphical Models Excerpt :

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

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

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.

Computer Vision Book

Computer Vision


  • Author : Simon J. D. Prince
  • Publisher : Cambridge University Press
  • Release Date : 2012-06-18
  • Genre: Computers
  • Pages : 580
  • ISBN 10 : 9781107011793

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Computer Vision Excerpt :

A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.

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

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Learning Probabilistic Graphical Models in R Excerpt :

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

Building Tractable Probabilistic Graphical Models for Computer Vision Problems Book

Building Tractable Probabilistic Graphical Models for Computer Vision Problems


  • Author : Xiangyang Lan
  • Publisher : Unknown
  • Release Date : 2007
  • Genre: Uncategoriezed
  • Pages : 198
  • ISBN 10 : CORNELL:31924108362496

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Building Tractable Probabilistic Graphical Models for Computer Vision Problems Excerpt :

Throughout this dissertation, we investigate the trade-off between model expressiveness and inference complexity in the context of several computer vision problems, including human pose recognition from a single image, articulated object detection and tracking, and image denoising. We construct graphical models with different structural complexity for these problems, and show experimental results to evaluate and compare their performance.

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

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Handbook of Graphical Models Excerpt :

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.

Probabilistic Graphical Models Book

Probabilistic Graphical Models


  • Author : Daphne Koller
  • Publisher : MIT Press
  • Release Date : 2009-07-31
  • Genre: Computers
  • Pages : 1270
  • ISBN 10 : 9780262013192

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Probabilistic Graphical Models Excerpt :

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

Graph Representation Learning Book

Graph Representation Learning


  • Author : William L. Hamilton
  • Publisher : Morgan & Claypool Publishers
  • Release Date : 2020-09-16
  • Genre: Computers
  • Pages : 159
  • ISBN 10 : 9781681739649

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Graph Representation Learning Excerpt :

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Computer Vision   ECCV 2008 Book

Computer Vision ECCV 2008


  • Author : David Forsyth
  • Publisher : Springer
  • Release Date : 2008-10-11
  • Genre: Computers
  • Pages : 826
  • ISBN 10 : 9783540886907

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Computer Vision ECCV 2008 Excerpt :

Welcome to the 2008EuropeanConference onComputer Vision. These proce- ings are the result of a great deal of hard work by many people. To produce them, a total of 871 papers were reviewed. Forty were selected for oral pres- tation and 203 were selected for poster presentation, yielding acceptance rates of 4.6% for oral, 23.3% for poster, and 27.9% in total. Weappliedthreeprinciples.First,sincewehadastronggroupofAreaChairs, the ?nal decisions to accept or reject a paper rested with the Area Chair, who wouldbeinformedbyreviewsandcouldactonlyinconsensuswithanotherArea Chair. Second, we felt that authors were entitled to a summary that explained how the Area Chair reached a decision for a paper. Third, we were very careful to avoid con?icts of interest. Each paper was assigned to an Area Chair by the Program Chairs, and each Area Chair received a pool of about 25 papers. The Area Chairs then identi?ed and rankedappropriatereviewersfor eachpaper in their pool, and a constrained optimization allocated three reviewers to each paper. We are very proud that every paper received at least three reviews. At this point, authors were able to respond to reviews. The Area Chairs then needed to reach a decision. We used a series of procedures to ensure careful review and to avoid con?icts of interest. ProgramChairs did not submit papers. The Area Chairs were divided into three groups so that no Area Chair in the group was in con?ict with any paper assigned to any Area Chair in the group.

Handbook of Pattern Recognition and Computer Vision Book

Handbook of Pattern Recognition and Computer Vision


  • Author : Chi-hau Chen
  • Publisher : World Scientific
  • Release Date : 2010
  • Genre: Computers
  • Pages : 796
  • ISBN 10 : 9789814273398

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Handbook of Pattern Recognition and Computer Vision Excerpt :

Both pattern recognition and computer vision have experienced rapid progress in the last twenty-five years. This book provides the latest advances on pattern recognition and computer vision along with their many applications. It features articles written by renowned leaders in the field while topics are presented in readable form to a wide range of readers. The book is divided into five parts: basic methods in pattern recognition, basic methods in computer vision and image processing, recognition applications, life science and human identification, and systems and technology. There are eight new chapters on the latest developments in life sciences using pattern recognition as well as two new chapters on pattern recognition in remote sensing.

Bayesian Reasoning and Machine Learning Book

Bayesian Reasoning and Machine Learning


  • Author : David Barber
  • Publisher : Cambridge University Press
  • Release Date : 2012-02-02
  • Genre: Computers
  • Pages : 697
  • ISBN 10 : 9780521518147

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Bayesian Reasoning and Machine Learning Excerpt :

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

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

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Probabilistic Graphical Models Excerpt :

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