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Probabilistic Graphical Models For Computer Vision

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Probabilistic Graphical Models for Computer Vision

Probabilistic Graphical Models for Computer Vision Book
Author : Qiang Ji
Publisher : Academic Press
Release : 2019-11
ISBN : 012803467X
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Luis Enrique Sucar
Publisher : Springer
Release : 2015-06-19
ISBN : 144716699X
Language : En, Es, Fr & De

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Book Description :

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.

Building Tractable Probabilistic Graphical Models for Computer Vision Problems

Building Tractable Probabilistic Graphical Models for Computer Vision Problems Book
Author : Xiangyang Lan
Publisher : Unknown
Release : 2007
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Daphne Koller,Nir Friedman
Publisher : MIT Press
Release : 2009-07-31
ISBN : 0262258358
Language : En, Es, Fr & De

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Book Description :

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.

Learning Structured Prediction Models in Computer Vision

Learning Structured Prediction Models in Computer Vision Book
Author : Fayao Liu
Publisher : Unknown
Release : 2015
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Most of the real world applications can be formulated as structured learning problems, in which the output domain can be arbitrary, e.g., a sequence or a graph. By modelling the structures (constraints and correlations) of the output variables, structured learning provides a more general learning scheme than simple binary classification or regression models. This thesis is dedicated to learning such structured prediction models, i.e., conditional random fields (CRFs) and their applications in computer vision. CRFs are popular probabilistic graphical models, which model the conditional distribution of the output variables given the observations. They play an essential role in the computer vision community and have found wide applications in various vision tasks-semantic labelling, object detection, pose estimation, to name a few. Specifically, we here focus on two challenging tasks in this thesis: image segmentation (also referred as semantic labelling) and depth estimation from single monocular images, which represent two types of CRFs models-discrete and continuous. In summary, we made three contributions in this thesis. First, we present a new approach to exploit tree potentials in CRFs for the task of image segmentation. This method combines the advantages of both CRFs and decision trees. Different from traditional methods, in which the potential functions of CRFs are defined as a linear combination of some pre-defined parametric models, we formulate the unary and the pairwise potentials as nonparametric forests-ensembles of decision trees, and learn the ensemble parameters and the trees in a unified optimization problem within the large-margin framework. In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs. Moreover, we learn class-wise decision trees for each object that appears in the image. We further show that this challenging optimization can be efficiently solved by combining a modified column generation and cutting-planes techniques. Experimental results on both binary and multi-class segmentation datasets demonstrate the power of the learned nonlinear nonparametric potentials. Second, we propose to model the unary potentials of the CRFs using a convolutional neural network (CNN). The deep CNN is trained on the large-scale ImageNet dataset and transferred to image segmentation here for constructing unary potentials of super-pixels. The CRFs parameters are then learned within the max-margin framework using structured support vector machines (SSVM). To fully exploit context information in inference, we construct spatially related co-occurrence pairwise potentials and incorporate them into the energy function. This prefers labellings of object pairs that frequently co-occur in a certain spatial layout and at the same time avoids implausible labellings during the inference. Extensive experiments on binary and multi-class segmentation benchmarks demonstrate the potentials of the proposed method. Third, different from the previous two works, we address the problem of continuous CRFs learning, applied to the task of depth estimation from single images. Specifically, we formulate and learn the unary and pairwise potentials of a continuous CRFs model with CNN networks in a unified framework. We term this new method as deep convolutional neural fields, abbreviated as DCNF. It jointly explores the capacity of deep CNN and continuous CRFs. The proposed method can be used for depth estimation of general scenes with no geometric priors nor any extra information injected. Specifically, in our case, the integral of the partition function can be calculated in a closed form such that we can exactly solve the log-likelihood maximization. Moreover, solving the inference problem for predicting depths of a test image is highly efficient as closed-form solutions exist. We then further propose an equally effective model based on fully convolutional networks and a novel superpixel pooling method, which is ~ 10 times faster, to speedup the patch-wise convolutions in the deep model. With this more efficient model, we are able to design very deep networks to pursue further performance gain. Experiments on both indoor and outdoor scene datasets demonstrate that the proposed method significantly outperforms state-of-the-art depth estimation approaches. We also show experimentally that the proposed method generalizes well to depth estimations of images unrelated to the training data. This indicates the potential of our method for benefiting other vision tasks.

Computer Vision

Computer Vision Book
Author : Simon J. D. Prince
Publisher : Cambridge University Press
Release : 2012-06-18
ISBN : 1107011795
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Anonim
Publisher : Unknown
Release : 2003
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Energy Minimization Methods in Computer Vision and Pattern Recognition book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Visual Analysis of Behaviour

Visual Analysis of Behaviour Book
Author : Shaogang Gong,Tao Xiang
Publisher : Springer Science & Business Media
Release : 2011-05-26
ISBN : 9780857296702
Language : En, Es, Fr & De

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Book Description :

This book presents a comprehensive treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives. Topics: covers learning-group activity models, unsupervised behaviour profiling, hierarchical behaviour discovery, learning behavioural context, modelling rare behaviours, and “man-in-the-loop” active learning; examines multi-camera behaviour correlation, person re-identification, and “connecting-the-dots” for abnormal behaviour detection; discusses Bayesian information criterion, Bayesian networks, “bag-of-words” representation, canonical correlation analysis, dynamic Bayesian networks, Gaussian mixtures, and Gibbs sampling; investigates hidden conditional random fields, hidden Markov models, human silhouette shapes, latent Dirichlet allocation, local binary patterns, locality preserving projection, and Markov processes; explores probabilistic graphical models, probabilistic topic models, space-time interest points, spectral clustering, and support vector machines.

Dissertation Abstracts International

Dissertation Abstracts International Book
Author : Anonim
Publisher : Unknown
Release : 2008
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Dissertation Abstracts International book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Global Variational Learning for Graphical Models with Latent Variables

Global Variational Learning for Graphical Models with Latent Variables Book
Author : Ahmed M. Abdelatty
Publisher : Unknown
Release : 2018
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Probabilistic Graphical Models have been used intensively for developing Machine Learning applications including Computer Vision, Natural Language processing, Collaborative Filtering, and Bioinformatics. Moreover, Graphical Models with latent variables are very powerful tools for modeling uncertainty, since latent variables can be used to represent unobserved factors, and they also can be used to model the correlations between the observed variables. However, global learning of Latent Variable Models (LVMs) is NP-hard in general, and the state-of-the-art algorithm for learning them such as Expectation Maximization algorithm can get stuck in local optimum. In this thesis, we address the problem of global variational learning for LVMs. More precisely, we propose a convex variational approximation for Maximum Likelihood Learning and apply Frank-Wolfe algorithm to solve it. We also investigate the use of the Global Optimization Algorithm (GOP) for Bayesian Learning, and we demonstrate that it converges to the global optimum.

Intelligence Science and Big Data Engineering

Intelligence Science and Big Data Engineering Book
Author : Changyin Sun,Fang Fang,Zhi-Hua Zhou,Wankou Yang,Zhiyong Liu
Publisher : Springer
Release : 2013-11-21
ISBN : 9783642420566
Language : En, Es, Fr & De

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Book Description :

This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013, held in Beijing, China, in July/August 2013. The 111 papers presented were carefully peer-reviewed and selected from 390 submissions. Topics covered include information theoretic and Bayesian approaches; probabilistic graphical models; pattern recognition and computer vision; signal processing and image processing; machine learning and computational intelligence; neural networks and neuro-informatics; statistical inference and uncertainty reasoning; bioinformatics and computational biology and speech recognition and natural language processing.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning Book
Author : Christopher M. Bishop
Publisher : Springer Verlag
Release : 2006-08-17
ISBN : 9780387310732
Language : En, Es, Fr & De

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Book Description :

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.

Query specific Learning and Inference for Probabilistic Graphical Models

Query specific Learning and Inference for Probabilistic Graphical Models Book
Author : Anton Chechetka
Publisher : Unknown
Release : 2011
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Abstract: "In numerous real world applications, from sensor networks to computer vision to natural text processing, one needs to reason about the system in question in the face of uncertainty. A key problem in all those settings is to compute the probability distribution over the variables of interest (the query) given the observed values of other random variables (the evidence). Probabilistic graphical models (PGMs) have become the approach of choice for representing and reasoning with high-dimensional probability distributions. However, for most models capable of accurately representing real-life distributions, inference is fundamentally intractable. As a result, optimally balancing the expressive power and inference complexity of the models, as well as designing better approximate inference algorithms, remain important open problems with potential to significantly improve the quality of answers to probabilistic queries. This thesis contributes algorithms for learning and approximate inference in probabilistic graphical models that improve on the state of the art by emphasizing the computational aspects of inference over the representational properties of the models. Our contributions fall into two categories: learning accurate models where exact inference is tractable and speeding up approximate inference by focusing computation on the query variables and only spending as much effort on the remaining parts of the model as needed to answer the query accurately. First, for a case when the set of evidence variables is not known in advance and a single model is needed that can be used to answer any query well, we propose a polynomial time algorithm for learning the structure of tractable graphical models with quality guarantees, including PAC learnability and graceful degradation guarantees. Ours is the first efficient algorithm to provide this type of guarantees. A key theoretical insight of our approach is a tractable upper bound on the mutual information of arbitrarily large sets of random variables that yields exponential speedups over the exact computation. Second, for a setting where the set of evidence variables is known in advance, we propose an approach for learning tractable models that tailors the structure of the model for the particular value of evidence that become known at test time. By avoiding a commitment to a single tractable structure during learning, we are able to expand the representation power of the model without sacrificing efficient exact inference and parameter learning. We provide a general framework that allows one to leverage existing structure learning algorithms for discovering high-quality evidence-specific structures. Empirically, we demonstrate state of the art accuracy on real-life datasets and an order of magnitude speedup. Finally, for applications where the intractable model structure is a given and approximate inference is needed, we propose a principled way to speed up convergence of belief propagation by focusing the computation on the query variables and away from the variables that are of no direct interest to the user. We demonstrate significant speedups over the state of the art on large-scale relational models. Unlike existing approaches, ours does not involve model simplification, and thus has an advantage of converging to the fixed point of the full model. More generally, we argue that the common approach of concentrating on the structure of representation provided by PGMs, and only structuring the computation as representation allows, is suboptimal because of the fundamental computational problems. It is the computation that eventually yields answers to the queries, so directly focusing on structure of computation is a natural direction for improving the quality of the answers. The results of this thesis are a step towards adapting the structure of computation as a foundation of graphical models."

Computer Vision

Computer Vision Book
Author : Simon Jeremy Damion Prince
Publisher : Unknown
Release : 2012
ISBN : 9781139518567
Language : En, Es, Fr & De

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Book Description :

"This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. [bullet] Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry [bullet] A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking [bullet] More than 70 algorithms are described in sufficient detail to implement [bullet] More than 350 full-color illustrations amplify the text [bullet] The treatment is self-contained, including all of the background mathematics [bullet] Additional resources at www.computervisionmodels.com"--

Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging

Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging Book
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
Publisher : Springer
Release : 2017-06-30
ISBN : 3319611887
Language : En, Es, Fr & De

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Book Description :

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.

Convergence Analysis of Reweighted Sum product Algorithms

Convergence Analysis of Reweighted Sum product Algorithms Book
Author : Tanya Gazelle Roosta
Publisher : Unknown
Release : 2008
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Convergence Analysis of Reweighted Sum product Algorithms book written by Tanya Gazelle Roosta, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Computer Vision ECCV

Computer Vision   ECCV     Book
Author : Anonim
Publisher : Unknown
Release : 2002
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Computer Vision ECCV book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Interactive Video

Interactive Video Book
Author : Riad Hammoud
Publisher : Springer Science & Business Media
Release : 2007-01-21
ISBN : 3540332154
Language : En, Es, Fr & De

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Book Description :

This book is a groundbreaking resource that covers both algorithms and technologies of interactive videos. It presents recent research and application work for building and browsing interactive digital videos. The book deals mainly with low-level semi-automatic and full-automatic processing of the video content for intelligent human computer interaction. There is a special focus on eye tracking methods.

Message Passing Algorithms for Facility Location Problems

Message Passing Algorithms for Facility Location Problems Book
Author : Nevena Lazic
Publisher : Unknown
Release : 2011
ISBN : 9780494777107
Language : En, Es, Fr & De

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Book Description :

Discrete location analysis is one of the most widely studied branches of operations research, whose applications arise in a wide variety of settings. This thesis describes a powerful new approach to facility location problems - that of message passing inference in probabilistic graphical models. Using this framework, we develop new heuristic algorithms, as well as a new approximation algorithm for a particular problem type.In machine learning applications, facility location can be seen a discrete formulation of clustering and mixture modeling problems. We apply the developed algorithms to such problems in computer vision. We tackle the problem of motion segmentation in video sequences by formulating it as a facility location instance and demonstrate the advantages of message passing algorithms over current segmentation methods.

Intelligent Data Analysis for Real Life Applications Theory and Practice

Intelligent Data Analysis for Real Life Applications  Theory and Practice Book
Author : Magdalena-Benedito, Rafael
Publisher : IGI Global
Release : 2012-06-30
ISBN : 1466618078
Language : En, Es, Fr & De

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Book Description :

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.