Download or read book Low Rank and Sparse Modeling for Visual Analysis written by Yun Fu and published by Springer. This book was released on 2014-10-30 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Low Rank Models in Visual Analysis written by Zhouchen Lin and published by Academic Press. This book was released on 2017-06-06 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Deep Learning through Sparse and Low Rank Modeling written by Zhangyang Wang and published by Academic Press. This book was released on 2019-04-26 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Low Rank Approximation written by Ivan Markovsky and published by Springer. This book was released on 2018-08-03 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Sparse Representation Modeling and Learning in Visual Recognition written by Hong Cheng and published by Springer. This book was released on 2015-05-25 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book High Dimensional and Low Quality Visual Information Processing written by Yue Deng and published by Springer. This book was released on 2014-09-04 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Artificial Intelligence and Security written by Xingming Sun and published by Springer Nature. This book was released on 2021-07-09 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNCS 12736-12737 constitutes the refereed proceedings of the 7th International Conference on Artificial Intelligence and Security, ICAIS 2021, which was held in Dublin, Ireland, in July 2021. The conference was formerly called “International Conference on Cloud Computing and Security” with the acronym ICCCS. The total of 93 full papers and 29 short papers presented in this two-volume proceedings was carefully reviewed and selected from 1013 submissions. Overall, a total of 224 full and 81 short papers were accepted for ICAIS 2021; the other accepted papers are presented in CCIS 1422-1424. The papers were organized in topical sections as follows: Part I: Artificial intelligence; and big data Part II: Big data; cloud computing and security; encryption and cybersecurity; information hiding; IoT security; and multimedia forensics

Download or read book Intelligent Data Engineering and Analytics written by Vikrant Bhateja and published by Springer Nature. This book was released on 2023-02-23 with total page 627 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents the proceedings of the 10th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2022), held at NIT Mizoram, Aizawl, Mizoram, India during 18 – 19 June 2022. Researchers, scientists, engineers, and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with prospective applications in various engineering disciplines in the book. These proceedings are divided into two volumes. It covers broad areas of information and decision sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. This volume is a valuable resource for postgraduate students in various engineering disciplines.

Download or read book Generalized Low Rank Models written by Madeleine Udell and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. This dissertation extends 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.

Download or read book Intelligence Science and Big Data Engineering Visual Data Engineering written by Zhen Cui and published by Springer Nature. This book was released on 2019-11-28 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Advances in Visual Computing written by George Bebis and published by Springer. This book was released on 2012-08-22 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNCS 7431 and 7432 constitutes the refereed proceedings of the 8th International Symposium on Visual Computing, ISVC 2012, held in Rethymnon, Crete, Greece, in July 2012. The 68 revised full papers and 35 poster papers presented together with 45 special track papers were carefully reviewed and selected from more than 200 submissions. The papers are organized in topical sections: Part I (LNCS 7431) comprises computational bioimaging; computer graphics; calibration and 3D vision; object recognition; illumination, modeling, and segmentation; visualization; 3D mapping, modeling and surface reconstruction; motion and tracking; optimization for vision, graphics, and medical imaging, HCI and recognition. Part II (LNCS 7432) comprises topics such as unconstrained biometrics: advances and trends; intelligent environments: algorithms and applications; applications; virtual reality; face processing and recognition.

Download or read book Cognitive and Neural Modelling for Visual Information Representation and Memorization written by Limiao Deng and published by CRC Press. This book was released on 2022-04-24 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on how visual information is represented, stored and extracted in the human brain, this book uses cognitive neural modeling in order to show how visual information is represented and memorized in the brain. Breaking through traditional visual information processing methods, the author combines our understanding of perception and memory from the human brain with computer vision technology, and provides a new approach for image recognition and classification. While biological visual cognition models and human brain memory models are established, applications such as pest recognition and carrot detection are also involved in this book. Given the range of topics covered, this book is a valuable resource for students, researchers and practitioners interested in the rapidly evolving field of neurocomputing, computer vision and machine learning.

Download or read book Magnetic Resonance Image Reconstruction written by Mehmet Akcakaya and published by Academic Press. This book was released on 2022-11-04 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. The book discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. This unique resource is suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI. Explains the underlying principles of MRI reconstruction, along with the latest research“/li> Gives example codes for some of the methods presented Includes updates on the latest developments, including compressed sensing, tensor-based reconstruction and machine learning based reconstruction

Download or read book Dictionary Learning in Visual Computing written by Qiang Zhang and published by Springer Nature. This book was released on 2022-05-31 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensions of K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One prominent application of dictionary learning is in the general field of visual computing, where long-standing challenges have seen promising new solutions based on sparse representation with learned dictionaries. With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, specific algorithms, and examples of applications for those who wish to have a quick start on this subject.

Download or read book Machine Learning Techniques on Gene Function Prediction Volume II written by Quan Zou and published by Frontiers Media SA. This book was released on 2023-04-11 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Neural Information Processing written by Derong Liu and published by Springer. This book was released on 2017-11-07 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.

Download or read book The Recent Advances in Transdisciplinary Data Science written by Henry Han and published by Springer Nature. This book was released on 2023-01-28 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First Southwest Data Science Conference, on The Recent Advances in Transdisciplinary Data Science, SDSC 2022, held in Waco, TX, USA, during March 25–26, 2022. The 14 full papers and 2 short papers included in this book were carefully reviewed and selected from 72 submissions. They were organized in topical sections as follows: Business and social data science; Health and biological data science; Applied data science, artificial intelligence, and data engineering.