# Probabilistic Graphical Models for Computer Vision

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## Probabilistic Graphical Models for Computer Vision

Author | : Qiang Ji |

Publsiher | : Academic Press |

Total Pages | : 294 |

Release | : 2019-11 |

ISBN 10 | : 012803467X |

ISBN 13 | : 9780128034675 |

Language | : EN, FR, DE, ES & NL |

**Probabilistic Graphical Models for Computer Vision Book Review:**

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

## Building Tractable Probabilistic Graphical Models for Computer Vision Problems

Author | : Xiangyang Lan |

Publsiher | : Anonim |

Total Pages | : 198 |

Release | : 2007 |

ISBN 10 | : |

ISBN 13 | : CORNELL:31924108362496 |

Language | : EN, FR, DE, ES & NL |

**Building Tractable Probabilistic Graphical Models for Computer Vision Problems Book Review:**

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.

## Probabilistic Graphical Models for Computer Vision.

Author | : Qiang Ji |

Publsiher | : Academic Press |

Total Pages | : 294 |

Release | : 2019-12-12 |

ISBN 10 | : 0128034955 |

ISBN 13 | : 9780128034958 |

Language | : EN, FR, DE, ES & NL |

**Probabilistic Graphical Models for Computer Vision. Book Review:**

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

Author | : Daphne Koller,Nir Friedman |

Publsiher | : MIT Press |

Total Pages | : 1231 |

Release | : 2009 |

ISBN 10 | : 0262013193 |

ISBN 13 | : 9780262013192 |

Language | : EN, FR, DE, ES & NL |

**Probabilistic Graphical Models Book Review:**

Proceedings of the annual Conference on Uncertainty in Artificial Intelligence, available for 1991-present. Since 1985, the Conference on Uncertainty in Artificial Intelligence (UAI) has been the primary international forum for exchanging results on the use of principled uncertain-reasoning methods in intelligent systems. The UAI Proceedings have become a basic reference for researches and practitioners who want to know about both theoretical advances and the latest applied developments in the field.

## Probabilistic Graphical Models

Author | : Luis Enrique Sucar |

Publsiher | : Springer |

Total Pages | : 253 |

Release | : 2015-06-19 |

ISBN 10 | : 144716699X |

ISBN 13 | : 9781447166993 |

Language | : EN, FR, DE, ES & NL |

**Probabilistic Graphical Models Book Review:**

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.

## Computer Vision

Author | : Simon J. D. Prince |

Publsiher | : Cambridge University Press |

Total Pages | : 580 |

Release | : 2012-06-18 |

ISBN 10 | : 1107011795 |

ISBN 13 | : 9781107011793 |

Language | : EN, FR, DE, ES & NL |

**Computer Vision Book Review:**

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

## Structured Learning and Prediction in Computer Vision

Author | : Sebastian Nowozin,Christoph H. Lampert |

Publsiher | : Now Publishers Inc |

Total Pages | : 196 |

Release | : 2011 |

ISBN 10 | : 1601984561 |

ISBN 13 | : 9781601984562 |

Language | : EN, FR, DE, ES & NL |

**Structured Learning and Prediction in Computer Vision Book Review:**

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

## Handbook Of Pattern Recognition And Computer Vision (2nd Edition)

Author | : Chen Chi Hau,Pau Louis-francois,Wang Patrick S P |

Publsiher | : World Scientific |

Total Pages | : 1044 |

Release | : 1999-03-12 |

ISBN 10 | : 9814497649 |

ISBN 13 | : 9789814497640 |

Language | : EN, FR, DE, ES & NL |

**Handbook Of Pattern Recognition And Computer Vision (2nd Edition) Book Review:**

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.

## Machine Learning

Author | : Kevin P. Murphy |

Publsiher | : MIT Press |

Total Pages | : 1067 |

Release | : 2012-08-24 |

ISBN 10 | : 0262018020 |

ISBN 13 | : 9780262018029 |

Language | : EN, FR, DE, ES & NL |

**Machine Learning Book Review:**

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.

## Handbook of Graphical Models

Author | : Marloes Maathuis,Mathias Drton,Steffen Lauritzen,Martin Wainwright |

Publsiher | : CRC Press |

Total Pages | : 536 |

Release | : 2018-11-12 |

ISBN 10 | : 0429874243 |

ISBN 13 | : 9780429874246 |

Language | : EN, FR, DE, ES & NL |

**Handbook of Graphical Models Book Review:**

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.

## Mastering Probabilistic Graphical Models Using Python

Author | : Ankur Ankan,Abinash Panda |

Publsiher | : Packt Publishing Ltd |

Total Pages | : 284 |

Release | : 2015-08-03 |

ISBN 10 | : 1784395218 |

ISBN 13 | : 9781784395216 |

Language | : EN, FR, DE, ES & NL |

**Mastering Probabilistic Graphical Models Using Python Book Review:**

Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python About This Book Gain in-depth knowledge of Probabilistic Graphical Models Model time-series problems using Dynamic Bayesian Networks A practical guide to help you apply PGMs to real-world problems Who This Book Is For If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian Learning or Probabilistic Graphical Models, this book will help you to understand the details of Graphical Models and use it in your data science problems. This book will also help you select the appropriate model as well as the appropriate algorithm for your problem. What You Will Learn Get to know the basics of Probability theory and Graph Theory Work with Markov Networks Implement Bayesian Networks Exact Inference Techniques in Graphical Models such as the Variable Elimination Algorithm Understand approximate Inference Techniques in Graphical Models such as Message Passing Algorithms Sample algorithms in Graphical Models Grasp details of Naive Bayes with real-world examples Deploy PGMs using various libraries in Python Gain working details of Hidden Markov Models with real-world examples In Detail Probabilistic Graphical Models is a technique in machine learning that uses the concepts of graph theory to compactly represent and optimally predict values in our data problems. In real world problems, it's often difficult to select the appropriate graphical model as well as the appropriate inference algorithm, which can make a huge difference in computation time and accuracy. Thus, it is crucial to know the working details of these algorithms. This book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also to run different inference algorithms on them. There is a complete chapter devoted to the most widely used networks Naive Bayes Model and Hidden Markov Models (HMMs). These models have been thoroughly discussed using real-world examples. Style and approach An easy-to-follow guide to help you understand Probabilistic Graphical Models using simple examples and numerous code examples, with an emphasis on more widely used models.

## Bayesian Reasoning and Machine Learning

Author | : David Barber |

Publsiher | : Cambridge University Press |

Total Pages | : 697 |

Release | : 2012-02-02 |

ISBN 10 | : 0521518148 |

ISBN 13 | : 9780521518147 |

Language | : EN, FR, DE, ES & NL |

**Bayesian Reasoning and Machine Learning Book Review:**

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

## Graphical Models

Author | : Michael Irwin Jordan,Terrence Joseph Sejnowski,Howard Hughes Medical Institute Computational Neurobiology Laboratory Terrence J Sejnowski,Tomaso A. Poggio |

Publsiher | : MIT Press |

Total Pages | : 421 |

Release | : 2001 |

ISBN 10 | : 9780262600422 |

ISBN 13 | : 0262600420 |

Language | : EN, FR, DE, ES & NL |

**Graphical Models Book Review:**

This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithm and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research. Graphical models use graphs to represent and manipulate joint probability distributions. They have their roots in artificial intelligence, statistics, and neural networks. The clean mathematical formalism of the graphical models framework makes it possible to understand a wide variety of network-based approaches to computation, and in particular to understand many neural network algorithms and architectures as instances of a broader probabilistic methodology. It also makes it possible to identify novel features of neural network algorithms and architectures and to extend them to more general graphical models.This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithms and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research. Contributors H. Attias, C. M. Bishop, B. J. Frey, Z. Ghahramani, D. Heckerman, G. E. Hinton, R. Hofmann, R. A. Jacobs, Michael I. Jordan, H. J. Kappen, A. Krogh, R. Neal, S. K. Riis, F. B. Rodríguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss

## Modeling and Analysis of Dependable Systems

Author | : Luigi Portinale,Daniele Codetta Raiteri |

Publsiher | : World Scientific |

Total Pages | : 272 |

Release | : 2015-06-09 |

ISBN 10 | : 9814612057 |

ISBN 13 | : 9789814612050 |

Language | : EN, FR, DE, ES & NL |

**Modeling and Analysis of Dependable Systems Book Review:**

The monographic volume addresses, in a systematic and comprehensive way, the state-of-the-art dependability (reliability, availability, risk and safety, security) of systems, using the Artificial Intelligence framework of Probabilistic Graphical Models (PGM). After a survey about the main concepts and methodologies adopted in dependability analysis, the book discusses the main features of PGM formalisms (like Bayesian and Decision Networks) and the advantages, both in terms of modeling and analysis, with respect to classical formalisms and model languages. Methodologies for deriving PGMs from standard dependability formalisms will be introduced, by pointing out tools able to support such a process. Several case studies will be presented and analyzed to support the suitability of the use of PGMs in the study of dependable systems. Contents:Dependability and ReliabilityProbabilistic Graphical ModelsFrom Fault Trees to Bayesian NetworksFrom Dynamic Fault Tree to Dynamic Bayesian NetworksDecision Theoretic DependabilityThe RADyBaN Tool: Supporting DependabilityCase Study 1: Cascading FailuresCase Study 2: Autonomous Fault Detection, Identification and RecoveryCase Study 3: Security Assessment in Critical InfrastructuresCase Study 4: Dynamic Reliability Keywords:Dependability;Reliability;Probabilistic Graphical Models;Bayesian Networks;Fault Detection Identification and Recovery

## Learning Probabilistic Graphical Models in R

Author | : David Bellot |

Publsiher | : Packt Publishing Ltd |

Total Pages | : 250 |

Release | : 2016-04-29 |

ISBN 10 | : 1784397415 |

ISBN 13 | : 9781784397418 |

Language | : EN, FR, DE, ES & NL |

**Learning Probabilistic Graphical Models in R Book Review:**

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 prediction. Next, you'll master using R packages and implementing its techniques. Finally, you'll be presented with machine learning applications that have a direct impact in many fields. Here, we'll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems. Style and approach This book gives you a detailed and step-by-step explanation of each mathematical concept, which will help you build and analyze your own machine learning models and apply them to real-world problems. The mathematics is kept simple and each formula is explained thoroughly.

## Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging

Author | : Henning Müller,B. Michael Kelm,Tal Arbel,Weidong Cai,M. Jorge Cardoso,Georg Langs,Bjoern Menze,Dimitris Metaxas,Albert Montillo,William M. Wells III,Shaoting Zhang,Albert C.S. Chung,Mark Jenkinson,Annemie Ribbens |

Publsiher | : Springer |

Total Pages | : 222 |

Release | : 2017-06-30 |

ISBN 10 | : 3319611887 |

ISBN 13 | : 9783319611884 |

Language | : EN, FR, DE, ES & NL |

**Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging Book Review:**

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in Athens, Greece, in October 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 13 papers presented in MCV workshop and the 6 papers presented in BAMBI workshop were carefully reviewed and selected from numerous submissions. The goal of the MCV workshop is to explore the use of "big data” algorithms for harvesting, organizing and learning from large-scale medical imaging data sets and for general-purpose automatic understanding of medical images. The BAMBI workshop aims to highlight the potential of using Bayesian or random field graphical models for advancing research in biomedical image analysis.

## Reasoning With Probabilistic and Deterministic Graphical Models

Author | : Rina Dechter |

Publsiher | : Synthesis Lectures on Artifici |

Total Pages | : 199 |

Release | : 2019-02-14 |

ISBN 10 | : 9781681734927 |

ISBN 13 | : 1681734923 |

Language | : EN, FR, DE, ES & NL |

**Reasoning With Probabilistic and Deterministic Graphical Models Book Review:**

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.

## Computer Vision - ECCV 2008

Author | : David Forsyth,Philip Torr |

Publsiher | : Springer Science & Business Media |

Total Pages | : 826 |

Release | : 2008-10-07 |

ISBN 10 | : 3540886893 |

ISBN 13 | : 9783540886891 |

Language | : EN, FR, DE, ES & NL |

**Computer Vision - ECCV 2008 Book Review:**

The four-volume set comprising LNCS volumes 5302/5303/5304/5305 constitutes the refereed proceedings of the 10th European Conference on Computer Vision, ECCV 2008, held in Marseille, France, in October 2008. The 243 revised papers presented were carefully reviewed and selected from a total of 871 papers submitted. The four books cover the entire range of current issues in computer vision. The papers are organized in topical sections on recognition, stereo, people and face recognition, object tracking, matching, learning and features, MRFs, segmentation, computational photography and active reconstruction.

## Graphical Models, Exponential Families, and Variational Inference

Author | : Martin J. Wainwright,Michael I. Jordan |

Publsiher | : Now Publishers Inc |

Total Pages | : 324 |

Release | : 2008 |

ISBN 10 | : 1601981848 |

ISBN 13 | : 9781601981844 |

Language | : EN, FR, DE, ES & NL |

**Graphical Models, Exponential Families, and Variational Inference Book Review:**

The core of this paper is a general set of variational principles for the problems of computing marginal probabilities and modes, applicable to multivariate statistical models in the exponential family.

## Markov Random Fields for Vision and Image Processing

Author | : Andrew Blake,Pushmeet Kohli,Carsten Rother |

Publsiher | : MIT Press |

Total Pages | : 472 |

Release | : 2011-07-22 |

ISBN 10 | : 0262297442 |

ISBN 13 | : 9780262297448 |

Language | : EN, FR, DE, ES & NL |

**Markov Random Fields for Vision and Image Processing Book Review:**

State-of-the-art research on MRFs, successful MRF applications, and advanced topics for future study. This volume demonstrates the power of the Markov random field (MRF) in vision, treating the MRF both as a tool for modeling image data and, utilizing recently developed algorithms, as a means of making inferences about images. These inferences concern underlying image and scene structure as well as solutions to such problems as image reconstruction, image segmentation, 3D vision, and object labeling. It offers key findings and state-of-the-art research on both algorithms and applications. After an introduction to the fundamental concepts used in MRFs, the book reviews some of the main algorithms for performing inference with MRFs; presents successful applications of MRFs, including segmentation, super-resolution, and image restoration, along with a comparison of various optimization methods; discusses advanced algorithmic topics; addresses limitations of the strong locality assumptions in the MRFs discussed in earlier chapters; and showcases applications that use MRFs in more complex ways, as components in bigger systems or with multiterm energy functions. The book will be an essential guide to current research on these powerful mathematical tools.