Aggelos K. Katsaggelos's Books

Super Resolution of Images and Video

By: Aggelos K. Katsaggelos,Rafael Molina,Javier Mateos

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Super Resolution of Images and Video

By: Aggelos K. Katsaggelos,Rafael Molina,Javier Mateos

This book focuses on the super resolution of images and video. The authors’ use of the term super resolution (SR) is used to describe the process of obtaining a high resolution (HR) image, or a sequence of HR images, from a set of low resolution (LR) observations. This process has also been referred to in the literature as resolution enhancement (RE). SR has been applied primarily to spatial and temporal RE, but also to hyperspectral image enhancement. This book concentrates on motion based spatial RE, although the authors also describe motion free and hyperspectral image SR problems. Also examined is the very recent research area of SR for compression, which consists of the intentional downsampling, during pre-processing, of a video sequence to be compressed and the application of SR techniques, during post-processing, on the compressed sequence. It is clear that there is a strong interplay between the tools and techniques developed for SR and a number of other inverse problems encountered in signal processing (e.g., image restoration, motion estimation). SR techniques are being applied to a variety of fields, such as obtaining improved still images from video sequences (video printing), high definition television, high performance color Liquid Crystal Display (LCD) screens, improvement of the quality of color images taken by one CCD, video surveillance, remote sensing, and medical imaging. The authors believe that the SR/RE area has matured enough to develop a body of knowledge that can now start to provide useful and practical solutions to challenging real problems and that SR techniques can be an integral part of an image and video codec and can drive the development of new coder-decoders (codecs) and standards.

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Page Count

134

Publisher

Springer Nature

Published Date

ISBN 10

3031022432

ISBN 13

9783031022432

Machine Learning Refined

Foundations, Algorithms, and Applications

By: Jeremy Watt,Reza Borhani,Aggelos K. Katsaggelos

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Machine Learning Refined

Foundations, Algorithms, and Applications

By: Jeremy Watt,Reza Borhani,Aggelos K. Katsaggelos

An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.

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597

Publisher

Cambridge University Press

Published Date

ISBN 10

1108480721

ISBN 13

9781108480727

Fundamentals of Machine Learning and Deep Learning in Medicine

By: Reza Borhani,Soheila Borhani,Aggelos K. Katsaggelos

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Fundamentals of Machine Learning and Deep Learning in Medicine

By: Reza Borhani,Soheila Borhani,Aggelos K. Katsaggelos

This book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine. Once an esoteric subject known to few outside of computer science and engineering departments, today artificial intelligence (AI) is a widely popular technology used by scholars from all across the academic universe. In particular, recent years have seen a great deal of interest in the AI subfields of machine learning and deep learning from researchers in medicine and life sciences, evidenced by the rapid growth in the number of articles published on the topic in peer-reviewed medical journals over the last decade. The demand for high-quality educational resources in this area has never been greater than it is today, and will only continue to grow at a rapid pace. Expert authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the reader’s learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their knowledge. This is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use this book without needing any additional prerequisites.

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Page Count

201

Publisher

Springer Nature

Published Date

ISBN 10

3031195027

ISBN 13

9783031195020

Joint Source-Channel Video Transmission

By: Fan Zhai,Aggelos Katsaggelos

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Joint Source-Channel Video Transmission

By: Fan Zhai,Aggelos Katsaggelos

This book deals with the problem of joint source-channel video transmission, i.e., the joint optimal allocation of resources at the application layer and the other network layers, such as data rate adaptation, channel coding, power adaptation in wireless networks, quality of service (QoS) support from the network, and packet scheduling, for efficient video transmission. Real-time video communication applications, such as videoconferencing, video telephony, and on-demand video streaming, have gained increased popularity. However, a key problem in video transmission over the existing Internet and wireless networks is the incompatibility between the nature of the network conditions and the QoS requirements (in terms, for example, of bandwidth, delay, and packet loss) of real-time video applications. To deal with this incompatibility, a natural approach is to adapt the end-system to the network. The joint source-channel coding approach aims to efficiently perform content-aware cross-layer resource allocation, thus increasing the communication efficiency of multiple network layers. Our purpose in this book is to review the basic elements of the state-of-the-art approaches toward joint source-channel video transmission for wired and wireless systems. In this book, we present a general resource-distortion optimization framework, which is used throughout the book to guide our discussions on various techniques of joint source-channel video transmission. In this framework, network resources from multiple layers are assigned to each video packet according to its level of importance. It provides not only an optimization benchmark against which the performance of other sub-optimal systems can be evaluated, but also a useful tool for assessing the effectiveness of different error control components in practical system design. This book is therefore written to be accessible to researchers, expert industrial R&D engineers, and university students who are interested in the cutting edge technologies in joint source-channel video transmission. Contents: Introduction / Elements of a Video Communication System / Joint Source-Channel Coding / Error-Resilient Video Coding / Channel Modeling and Channel Coding / Internet Video Transmission / Wireless Video Transmission / Conclusions

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Springer Nature

Published Date

ISBN 10

3031022440

ISBN 13

9783031022449

Rate-Distortion Based Video Compression

Optimal Video Frame Compression and Object Boundary Encoding

By: Guido M. Schuster,Aggelos Katsaggelos

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Rate-Distortion Based Video Compression

Optimal Video Frame Compression and Object Boundary Encoding

By: Guido M. Schuster,Aggelos Katsaggelos

One of the most intriguing problems in video processing is the removal of the redundancy or the compression of a video signal. There are a large number of applications which depend on video compression. Data compression represents the enabling technology behind the multimedia and digital television revolution. In motion compensated lossy video compression the original video sequence is first split into three new sources of information, segmentation, motion and residual error. These three information sources are then quantized, leading to a reduced rate for their representation but also to a distorted reconstructed video sequence. After the decomposition of the original source into segmentation, mo tion and residual error information is decided, the key remaining problem is the allocation of the available bits into these three sources of information. In this monograph a theory is developed which provides a solution to this fundamental bit allocation problem. It can be applied to all quad-tree-based motion com pensated video coders which use a first order differential pulse code modulation (DPCM) scheme for the encoding of the displacement vector field (DVF) and a block-based transform scheme for the encoding of the displaced frame differ ence (DFD). An optimal motion estimator which results in the smallest DFD energy for a given bit rate for the encoding of the DVF is also a result of this theory. Such a motion estimator is used to formulate a motion compensated interpolation scheme which incorporates a global smoothness constraint for the DVF.

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Page Count

305

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1475725663

ISBN 13

9781475725667

Book's by Aggelos K. Katsaggelos

Super Resolution of Images and Video

Super Resolution of Images and Video

Machine Learning Refined

Machine Learning Refined

Fundamentals of Machine Learning and Deep Learning in Medicine

Fundamentals of Machine Learning and Deep Learning in Medicine

Joint Source-Channel Video Transmission

Joint Source-Channel Video Transmission

Rate-Distortion Based Video Compression

Rate-Distortion Based Video Compression

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