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Low-Rank Models in Visual Analysis

Low-Rank Models in Visual Analysis
  • Author : Zhouchen Lin,Hongyang Zhang
  • Publisher :Unknown
  • Release Date :2017-06-06
  • Total pages :260
  • ISBN : 9780128127322
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Summary : Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications presents the state-of-the-art on low-rank models and their application to visual analysis. It provides insight into the ideas behind the models and their algorithms, giving details of their formulation and deduction. The main applications included are video denoising, background modeling, image alignment and rectification, motion segmentation, image segmentation and image saliency detection. Readers will learn which Low-rank models are highly useful in practice (both linear and nonlinear models), how to solve low-rank models efficiently, and how to apply low-rank models to real problems. Presents a self-contained, up-to-date introduction that covers underlying theory, algorithms and the state-of-the-art in current applications Provides a full and clear explanation of the theory behind the models Includes detailed proofs in the appendices

Low-Rank and Sparse Modeling for Visual Analysis

Low-Rank and Sparse Modeling for Visual Analysis
  • Author : Yun Fu
  • Publisher :Unknown
  • Release Date :2014-10-30
  • Total pages :236
  • ISBN : 9783319120003
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Summary : This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.

Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse and Low-Rank Modeling
  • Author : Zhangyang Wang,Yun Fu,Thomas S. Huang
  • Publisher :Unknown
  • Release Date :2019-04-11
  • Total pages :296
  • ISBN : 9780128136607
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Summary : Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications

Probabilistic Graphical Models for Computer Vision

Probabilistic Graphical Models for Computer Vision
  • Author : Qiang Ji
  • Publisher :Unknown
  • Release Date :2019-11
  • Total pages :294
  • ISBN : 9780128034675
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Summary : Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and teaches how to develop the PGM model from training data. This book discusses PGMs and their significance in the context of solving computer vision problems, giving the basic concepts, definitions and properties. It also provides a comprehensive introduction to well-established theories for different types of PGMs, including both directed and undirected PGMs, such as Bayesian Networks, Markov Networks and their variants. Discusses PGM theories and techniques with computer vision examples Focuses on well-established PGM theories that are accompanied by corresponding pseudocode for computer vision Includes an extensive list of references, online resources and a list of publicly available and commercial software Covers computer vision tasks, including feature extraction and image segmentation, object and facial recognition, human activity recognition, object tracking and 3D reconstruction

Computer Vision – ECCV 2012

Computer Vision – ECCV 2012
  • Author : Andrew Fitzgibbon,Svetlana Lazebnik,Pietro Perona,Yoichi Sato,Cordelia Schmid
  • Publisher :Unknown
  • Release Date :2012-09-26
  • Total pages :893
  • ISBN : 9783642337833
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Summary : The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.

Sparse Representation, Modeling and Learning in Visual Recognition

Sparse Representation, Modeling and Learning in Visual Recognition
  • Author : Hong Cheng
  • Publisher :Unknown
  • Release Date :2015-05-25
  • Total pages :257
  • ISBN : 9781447167143
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Summary : This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.

Accelerated Optimization for Machine Learning

Accelerated Optimization for Machine Learning
  • Author : Zhouchen Lin,Huan Li,Cong Fang
  • Publisher :Unknown
  • Release Date :2020-05-29
  • Total pages :275
  • ISBN : 9789811529108
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Summary : This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.

Generalized Low Rank Models

Generalized Low Rank Models
  • Author : Madeleine Udell,Corinne Horn,Reza Zadeh,Stephen Boyd
  • Publisher :Unknown
  • Release Date :2016-05-03
  • Total pages :142
  • ISBN : 1680831402
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Summary : Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well-known techniques in data analysis, such as nonnegative matrix factorization, matrix completion, sparse and robust PCA, k-means, k-SVD, and maximum margin matrix factorization. The method handles heterogeneous data sets, and leads to coherent schemes for compressing, denoising, and imputing missing entries across all data types simultaneously. It also admits a number of interesting interpretations of the low rank factors, which allow clustering of examples or of features. We propose several parallel algorithms for fitting generalized low rank models, and describe implementations and numerical results.

Handbook of Robust Low-Rank and Sparse Matrix Decomposition

Handbook of Robust Low-Rank and Sparse Matrix Decomposition
  • Author : Thierry Bouwmans,Necdet Serhat Aybat,El-hadi Zahzah
  • Publisher :Unknown
  • Release Date :2016-09-20
  • Total pages :520
  • ISBN : 9781315353531
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Summary : Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques. Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance. With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.

Low-Rank Approximation

Low-Rank Approximation
  • Author : Ivan Markovsky
  • Publisher :Unknown
  • Release Date :2018-08-03
  • Total pages :272
  • ISBN : 9783319896205
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Summary : This book is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. A major part of the text is devoted to application of the theory with a range of applications from systems and control theory to psychometrics being described. Special knowledge of the application fields is not required. The second edition of /Low-Rank Approximation/ is a thoroughly edited and extensively rewritten revision. It contains new chapters and sections that introduce the topics of: • variable projection for structured low-rank approximation;• missing data estimation;• data-driven filtering and control;• stochastic model representation and identification;• identification of polynomial time-invariant systems; and• blind identification with deterministic input model. The book is complemented by a software implementation of the methods presented, which makes the theory directly applicable in practice. In particular, all numerical examples in the book are included in demonstration files and can be reproduced by the reader. This gives hands-on experience with the theory and methods detailed. In addition, exercises and MATLAB^® /Octave examples will assist the reader quickly to assimilate the theory on a chapter-by-chapter basis. “Each chapter is completed with a new section of exercises to which complete solutions are provided.” Low-Rank Approximation (second edition) is a broad survey of the Low-Rank Approximation theory and applications of its field which will be of direct interest to researchers in system identification, control and systems theory, numerical linear algebra and optimization. The supplementary problems and solutions render it suitable for use in teaching graduate courses in those subjects as well.

Pattern Recognition And Big Data

Pattern Recognition And Big Data
  • Author : Pal Sankar Kumar,Pal Amita
  • Publisher :Unknown
  • Release Date :2016-12-15
  • Total pages :876
  • ISBN : 9789813144569
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Summary : Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications. Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis.

Nonlinear Model Predictive Control

Nonlinear Model Predictive Control
  • Author : Lalo Magni,Davide Martino Raimondo,Frank Allgöwer
  • Publisher :Unknown
  • Release Date :2009-05-25
  • Total pages :576
  • ISBN : 9783642010934
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Summary : Over the past few years significant progress has been achieved in the field of nonlinear model predictive control (NMPC), also referred to as receding horizon control or moving horizon control. More than 250 papers have been published in 2006 in ISI Journals. With this book we want to bring together the contributions of a diverse group of internationally well recognized researchers and industrial practitioners, to critically assess the current status of the NMPC field and to discuss future directions and needs. The book consists of selected papers presented at the International Workshop on Assessment an Future Directions of Nonlinear Model Predictive Control that took place from September 5 to 9, 2008, in Pavia, Italy.

High-Dimensional and Low-Quality Visual Information Processing

High-Dimensional and Low-Quality Visual Information Processing
  • Author : Yue Deng
  • Publisher :Unknown
  • Release Date :2014-09-04
  • Total pages :99
  • ISBN : 9783662445266
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Summary : This thesis primarily focuses on how to carry out intelligent sensing and understand the high-dimensional and low-quality visual information. After exploring the inherent structures of the visual data, it proposes a number of computational models covering an extensive range of mathematical topics, including compressive sensing, graph theory, probabilistic learning and information theory. These computational models are also applied to address a number of real-world problems including biometric recognition, stereo signal reconstruction, natural scene parsing, and SAR image processing.

Feature Extraction

Feature Extraction
  • Author : Isabelle Guyon,Steve Gunn,Masoud Nikravesh,Lofti A. Zadeh
  • Publisher :Unknown
  • Release Date :2008-11-16
  • Total pages :778
  • ISBN : 9783540354888
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Summary : This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons.

The Chemistry of Low-rank Coals

The Chemistry of Low-rank Coals
  • Author : Harold H. Schobert,American Chemical Society. Meeting
  • Publisher :Unknown
  • Release Date :1984
  • Total pages :315
  • ISBN : UCAL:B4125530
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Summary : Good,No Highlights,No Markup,all pages are intact, Slight Shelfwear,may have the corners slightly dented, may have slight color changes/slightly damaged spine.

Journal of the American Statistical Association

Journal of the American Statistical Association
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :2008
  • Total pages :229
  • ISBN : STANFORD:36105131553427
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Summary :

Fuel Property Estimation and Combustion Process Characterization

Fuel Property Estimation and Combustion Process Characterization
  • Author : Yen-Hsiung Kiang
  • Publisher :Unknown
  • Release Date :2018-02-20
  • Total pages :460
  • ISBN : 9780128134740
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Summary : Fuel Property Estimation and Combustion Process Characterization is a thorough tool book, which provides readers with the most up-to-date, valuable methodologies to efficiently and cost-effectively attain useful properties of all types of fuels and achieve combustion process characterizations for more efficient design and better operation. Through extensive experience in fuels and combustion, Kiang has developed equations and methodologies that can readily obtain reasonable properties for all types of fuels (including wastes and biomass), which enable him to provide guidance for designers and operators in the combustion field, in order to ensure the design, operation, and diagnostics of all types of combustion systems are of the highest quality and run at optimum efficiency. Written for professionals and researchers in the renewable energy, combustion, chemical, and mechanical engineering fields, the information in this book will equip readers with detailed guidance on how to reliably obtain properties of fuels quickly for the design, operation and diagnostics of combustion systems to achieve highly efficient combustion processes. Presents models for quick estimation of fuel properties without going through elaborate, costly and time consuming sampling and laboratory testing Offers methodologies to determine combustion process characteristics for designing and deploying combustion systems Examines the fundamentals of combustion applied to energy systems, including thermodynamics of traditional and alternative fuels combustion Presents a fuel property database for over 1400 fuels Includes descriptive application of big data technology, using dual properties analysis as an example Provides specific technical solutions for combustion, fuels and waste processing

Spatial Modeling in GIS and R for Earth and Environmental Sciences

Spatial Modeling in GIS and R for Earth and Environmental Sciences
  • Author : Hamid Reza Pourghasemi,Candan Gokceoglu
  • Publisher :Unknown
  • Release Date :2019-01-18
  • Total pages :798
  • ISBN : 9780128156957
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Summary : Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling. Offers a clear, interdisciplinary guide to serve researchers in a variety of fields, including hazards, land surveying, remote sensing, cartography, geophysics, geology, natural resources, environment and geography Provides an overview, methods and case studies for each application Expresses concepts and methods at an appropriate level for both students and new users to learn by example

Computer Vision - ACCV 2010

Computer Vision - ACCV 2010
  • Author : Ron Kimmel,Reinhard Klette,Akihiro Sugimoto
  • Publisher :Unknown
  • Release Date :2011-03-14
  • Total pages :790
  • ISBN : 9783642193170
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Summary : The four-volume set LNCS 6492-6495 constitutes the thoroughly refereed post-proceedings of the 10th Asian Conference on Computer Vision, ACCV 2009, held in Queenstown, New Zealand in November 2010. All together the four volumes present 206 revised papers selected from a total of 739 Submissions. All current issues in computer vision are addressed ranging from algorithms that attempt to automatically understand the content of images, optical methods coupled with computational techniques that enhance and improve images, and capturing and analyzing the world's geometry while preparing the higher level image and shape understanding. Novel gemometry techniques, statistical learning methods, and modern algebraic procedures are dealt with as well.

Computer Vision for Microscopy Image Analysis

Computer Vision for Microscopy Image Analysis
  • Author : Mei Chen
  • Publisher :Unknown
  • Release Date :2020-12-01
  • Total pages :228
  • ISBN : 9780128149737
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Summary : Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts. Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information. Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation. This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection. Discover how computer vision can automate and enhance the human assessment of microscopy images for discovery Grasp the state-of-the-art approaches, especially deep neural networks Learn where to obtain open-source datasets and software to jumpstart his or her own investigation

Exploring SAS Viya

Exploring SAS Viya
  • Author : Sas Education
  • Publisher :Unknown
  • Release Date :2019-06-28
  • Total pages :110
  • ISBN : 164295490X
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Summary : Data visualization enables decision makers to see analytics presented visually so that they can grasp difficult concepts or identify new patterns. SAS offers several solutions for visualizing your data, many of which are powered by SAS Viya. This book includes four visualization solutions powered by SAS Viya: SAS Visual Analytics, SAS Visual Statistics, SAS Visual Text Analytics, and SAS Visual Investigator. SAS visualization software is designed for anyone in your organization who wants to use and derive insights from data-from influencers, decision makers, and analysts to statisticians and data scientists. Also available as a free e-book from sas.com/books.