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Probabilistic Graphical Models for Computer Vision

Probabilistic Graphical Models for Computer Vision
A Book

by Qiang Ji

  • Publisher : Academic Press
  • Release : 2019-11
  • Pages : 294
  • ISBN : 012803467X
  • Language : En, Es, Fr & De
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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

Probabilistic Graphical Models
Principles and Applications

by Luis Enrique Sucar

  • Publisher : Springer Nature
  • Release : 2020-12-23
  • Pages : 355
  • ISBN : 3030619435
  • Language : En, Es, Fr & De
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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 find the book to be an invaluable reference. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.

Building Tractable Probabilistic Graphical Models for Computer Vision Problems

Building Tractable Probabilistic Graphical Models for Computer Vision Problems
A Book

by Xiangyang Lan

  • Publisher : Unknown Publisher
  • Release : 2007
  • Pages : 198
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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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

Probabilistic Graphical Models
Principles and Techniques

by Daphne Koller,Nir Friedman

  • Publisher : MIT Press
  • Release : 2009
  • Pages : 1231
  • ISBN : 0262013193
  • Language : En, Es, Fr & De
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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.

Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging

Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging
MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers

by 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

  • Publisher : Springer
  • Release : 2017-06-30
  • Pages : 222
  • ISBN : 3319611887
  • Language : En, Es, Fr & De
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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.

Computer Vision

Computer Vision
Models, Learning, and Inference

by Simon J. D. Prince

  • Publisher : Cambridge University Press
  • Release : 2012-06-18
  • Pages : 580
  • ISBN : 1107011795
  • Language : En, Es, Fr & De
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A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.

Energy Minimization Methods in Computer Vision and Pattern Recognition

Energy Minimization Methods in Computer Vision and Pattern Recognition
... International Workshop EMMCVPR ... Proceedings

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2003
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Dissertation Abstracts International

Dissertation Abstracts International
The sciences and engineering. B

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2008
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Proceedings of the Workshop on Computer Vision--Representation and Control, April 30-May 2, 1984, Hilton Hotel, Annapolis, Maryland

Proceedings of the Workshop on Computer Vision--Representation and Control, April 30-May 2, 1984, Hilton Hotel, Annapolis, Maryland
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1984
  • Pages : 257
  • ISBN : 9780818685316
  • Language : En, Es, Fr & De
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Structured Learning and Prediction in Computer Vision

Structured Learning and Prediction in Computer Vision
A Book

by Sebastian Nowozin,Christoph H. Lampert

  • Publisher : Now Publishers Inc
  • Release : 2011
  • Pages : 196
  • ISBN : 1601984561
  • Language : En, Es, Fr & De
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Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision.

Computer Vision - ECCV ...

Computer Vision - ECCV ...
... European Conference on Computer Vision ... : Proceedings

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2002
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Proceedings

Proceedings
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2003
  • Pages : 1530
  • ISBN : 9780769519500
  • Language : En, Es, Fr & De
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Convergence Analysis of Reweighted Sum-product Algorithms

Convergence Analysis of Reweighted Sum-product Algorithms
A Book

by Tanya Gazelle Roosta

  • Publisher : Unknown Publisher
  • Release : 2008
  • Pages : 86
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Bayesian Network Technologies: Applications and Graphical Models

Bayesian Network Technologies: Applications and Graphical Models
Applications and Graphical Models

by Mittal, Ankush,Kassim, Ashraf

  • Publisher : IGI Global
  • Release : 2007-03-31
  • Pages : 368
  • ISBN : 159904143X
  • Language : En, Es, Fr & De
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"This book provides an excellent, well-balanced collection of areas where Bayesian networks have been successfully applied; it describes the underlying concepts of Bayesian Networks with the help of diverse applications, and theories that prove Bayesian networks valid"--Provided by publisher.

Data-Driven Computational Neuroscience

Data-Driven Computational Neuroscience
Machine Learning and Statistical Models

by Concha Bielza,Pedro Larrañaga

  • Publisher : Cambridge University Press
  • Release : 2020-11-26
  • Pages : 708
  • ISBN : 110849370X
  • Language : En, Es, Fr & De
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Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.

Machine Learning

Machine Learning
A Probabilistic Perspective

by Kevin P. Murphy

  • Publisher : MIT Press
  • Release : 2012-08-24
  • Pages : 1067
  • ISBN : 0262018020
  • Language : En, Es, Fr & De
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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.

Text, Speech and Dialogue

Text, Speech and Dialogue
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2003
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Advances in Bayesian Networks

Advances in Bayesian Networks
A Book

by José A. Gámez,Serafin Moral,Antonio Salmerón Cerdan

  • Publisher : Springer
  • Release : 2013-06-29
  • Pages : 328
  • ISBN : 3540398791
  • Language : En, Es, Fr & De
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In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.

Intelligent Data Analysis for Real-Life Applications: Theory and Practice

Intelligent Data Analysis for Real-Life Applications: Theory and Practice
Theory and Practice

by Magdalena-Benedito, Rafael

  • Publisher : IGI Global
  • Release : 2012-06-30
  • Pages : 444
  • ISBN : 1466618078
  • Language : En, Es, Fr & De
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With the recent and enormous increase in the amount of available data sets of all kinds, applying effective and efficient techniques for analyzing and extracting information from that data has become a crucial task. Intelligent Data Analysis for Real-Life Applications: Theory and Practice investigates the application of Intelligent Data Analysis (IDA) to these data sets through the design and development of algorithms and techniques to extract knowledge from databases. This pivotal reference explores practical applications of IDA, and it is essential for academic and research libraries as well as students, researchers, and educators in data analysis, application development, and database management.

Intelligence Science and Big Data Engineering. Visual Data Engineering

Intelligence Science and Big Data Engineering. Visual Data Engineering
9th International Conference, IScIDE 2019, Nanjing, China, October 17–20, 2019, Proceedings, Part I

by Zhen Cui,Jinshan Pan,Shanshan Zhang,Liang Xiao,Jian Yang

  • Publisher : Springer Nature
  • Release : 2019-11-28
  • Pages : 577
  • ISBN : 3030361896
  • Language : En, Es, Fr & De
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The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019. The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.