Low Rank Models in Visual Analysis

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

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  • Author : Zhouchen Lin
  • Publisher : Academic Press
  • Pages : 260 pages
  • ISBN : 0128127325
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKLow Rank Models in Visual Analysis

Low-Rank Models in Visual Analysis

Low-Rank Models in Visual Analysis
  • Author : Zhouchen Lin,Hongyang Zhang
  • Publisher : Academic Press
  • Release : 06 June 2017
GET THIS BOOKLow-Rank Models in Visual Analysis

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

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 : Academic Press
  • Release : 11 April 2019
GET THIS BOOKDeep Learning through Sparse and Low-Rank Modeling

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

Low-Rank and Sparse Modeling for Visual Analysis

Low-Rank and Sparse Modeling for Visual Analysis
  • Author : Yun Fu
  • Publisher : Springer
  • Release : 30 October 2014
GET THIS BOOKLow-Rank and Sparse Modeling for Visual Analysis

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.

Vision Models for High Dynamic Range and Wide Colour Gamut Imaging

Vision Models for High Dynamic Range and Wide Colour Gamut Imaging
  • Author : Marcelo Bertalmío
  • Publisher : Academic Press
  • Release : 06 November 2019
GET THIS BOOKVision Models for High Dynamic Range and Wide Colour Gamut Imaging

To enhance the overall viewing experience (for cinema, TV, games, AR/VR) the media industry is continuously striving to improve image quality. Currently the emphasis is on High Dynamic Range (HDR) and Wide Colour Gamut (WCG) technologies, which yield images with greater contrast and more vivid colours. The uptake of these technologies, however, has been hampered by the significant challenge of understanding the science behind visual perception. Vision Models for High Dynamic Range and Wide Colour Gamut Imaging provides university

Multimodal Behavior Analysis in the Wild

Multimodal Behavior Analysis in the Wild
  • Author : Xavier Alameda-Pineda,Elisa Ricci,Nicu Sebe
  • Publisher : Academic Press
  • Release : 13 November 2018
GET THIS BOOKMultimodal Behavior Analysis in the Wild

Multimodal Behavioral Analysis in the Wild: Advances and Challenges presents the state-of- the-art in behavioral signal processing using different data modalities, with a special focus on identifying the strengths and limitations of current technologies. The book focuses on audio and video modalities, while also emphasizing emerging modalities, such as accelerometer or proximity data. It covers tasks at different levels of complexity, from low level (speaker detection, sensorimotor links, source separation), through middle level (conversational group detection, addresser and addressee identification),

Computer Vision for Microscopy Image Analysis

Computer Vision for Microscopy Image Analysis
  • Author : Mei Chen
  • Publisher : Academic Press
  • Release : 01 December 2020
GET THIS BOOKComputer Vision for Microscopy Image Analysis

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

Spectral Geometry of Shapes

Spectral Geometry of Shapes
  • Author : Jing Hua,Zichun Zhong
  • Publisher : Academic Press
  • Release : 15 January 2020
GET THIS BOOKSpectral Geometry of Shapes

Spectral Geometry of Shapes presents unique shape analysis approaches based on shape spectrum in differential geometry. It provides insights on how to develop geometry-based methods for 3D shape analysis. The book is an ideal learning resource for graduate students and researchers in computer science, computer engineering and applied mathematics who have an interest in 3D shape analysis, shape motion analysis, image analysis, medical image analysis, computer vision and computer graphics. Due to the rapid advancement of 3D acquisition technologies there

Probabilistic Graphical Models for Computer Vision

Probabilistic Graphical Models for Computer Vision
  • Author : Qiang Ji
  • Publisher : Academic Press
  • Release : 01 November 2019
GET THIS BOOKProbabilistic Graphical Models for Computer Vision

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

Cardiovascular and Coronary Artery Imaging

Cardiovascular and Coronary Artery Imaging
  • Author : Ayman S. El-Baz,Jasjit S. Suri
  • Publisher : Academic Press
  • Release : 24 November 2021
GET THIS BOOKCardiovascular and Coronary Artery Imaging

Cardiovascular and Coronary Artery Imaging, Volume One covers state-of-the-art approaches for automated non-invasive systems in early cardiovascular disease diagnosis. The book includes several prominent imaging modalities, such as MRI, CT and PET technologies. A special emphasis is placed on automated imaging analysis techniques, which are important to biomedical imaging analysis of the cardiovascular system. This is a comprehensive, multi-contributed reference work that details the latest developments in spatial, temporal and functional cardiac imaging. Takes an integrated approach to cardiovascular and

Accelerated Optimization for Machine Learning

Accelerated Optimization for Machine Learning
  • Author : Zhouchen Lin,Huan Li,Cong Fang
  • Publisher : Springer Nature
  • Release : 29 May 2020
GET THIS BOOKAccelerated Optimization for Machine Learning

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

Skeletonization

Skeletonization
  • Author : Punam K Saha,Gunilla Borgefors,Gabriella Sanniti di Baja
  • Publisher : Academic Press
  • Release : 06 June 2017
GET THIS BOOKSkeletonization

Skeletonization: Theory, Methods and Applications is a comprehensive reference on skeletonization, written by the world’s leading researchers in the field. The book presents theory, methods, algorithms and their evaluation, together with applications. Skeletonization is used in many image processing and computer vision applications such as shape recognition and analysis, shape decomposition and character recognition, as well as medical imaging for pulmonary, cardiac, mammographic applications. Part I includes theories and methods unique to skeletonization. Part II includes novel applications including

Computer Vision for Assistive Healthcare

Computer Vision for Assistive Healthcare
  • Author : Leo Marco,Giovanni Maria Farinella
  • Publisher : Academic Press
  • Release : 15 May 2018
GET THIS BOOKComputer Vision for Assistive Healthcare

Computer Vision for Assistive Healthcare describes how advanced computer vision techniques provide tools to support common human needs, such as mental functioning, personal mobility, sensory functions, daily living activities, image processing, pattern recognition, machine learning and how language processing and computer graphics cooperate with robotics to provide such tools. Users will learn about the emerging computer vision techniques for supporting mental functioning, algorithms for analyzing human behavior, and how smart interfaces and virtual reality tools lead to the development of

Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics
  • Author : Felix Fritzen,David Ryckelynck
  • Publisher : MDPI
  • Release : 18 September 2019
GET THIS BOOKMachine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In

Low-Rank Approximation

Low-Rank Approximation
  • Author : Ivan Markovsky
  • Publisher : Springer
  • Release : 03 August 2018
GET THIS BOOKLow-Rank Approximation

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

Sparse Representation, Modeling and Learning in Visual Recognition

Sparse Representation, Modeling and Learning in Visual Recognition
  • Author : Hong Cheng
  • Publisher : Springer
  • Release : 25 May 2015
GET THIS BOOKSparse Representation, Modeling and Learning in Visual Recognition

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,