Get a broad overview of the different modalities of immersive video technologies-from omnidirectional video to light fields and volumetric video-from a multimedia processing perspective. From capture to representation, coding, and display, video technologies have been evolving significantly and in many different directions over the last few decades, with the ultimate goal of providing a truly immersive experience to users. After setting up a common background for these technologies, based on the plenoptic function theoretical concept, Immersive Video Technologies offers a comprehensive overview of the leading technologies enabling visual immersion, including omnidirectional (360 degrees) video, light fields, and volumetric video. Following the critical components of the typical content production and delivery pipeline, the book presents acquisition, representation, coding, rendering, and quality assessment approaches for each immersive video modality. The text also reviews current standardization efforts and explores new research directions. With this book the reader will a) gain a broad understanding of immersive video technologies that use three different modalities: omnidirectional video, light fields, and volumetric video; b) learn about the most recent scientific results in the field, including the recent learning-based methodologies; and c) understand the challenges and perspectives for immersive video technologies.

This is nothing less than a totally essential reference for engineers and researchers in any field of work that involves the use of compressed imagery. Beginning with a thorough and up-to-date overview of the fundamentals of image compression, the authors move on to provide a complete description of the JPEG2000 standard. They then devote space to the implementation and exploitation of that standard. The final section describes other key image compression systems. This work has specific applications for those involved in the development of software and hardware solutions for multimedia, internet, and medical imaging applications. Included is a CD-ROM that provides a complete C++ implementation of JPEG2000 Part 1.

This book provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field. Even though there have been a number of different approaches proposed in the literature for face recognition based on deep learning methods, there is not a single book available in the literature that gives a complete overview of these methods. The proposed book captures the state of the art in face recognition using various deep learning methods, and it covers a variety of different topics related to face recognition. This book is aimed at graduate students studying electrical engineering and/or computer science. Biometrics is a course that is widely offered at both undergraduate and graduate levels at many institutions around the world: This book can be used as a textbook for teaching topics related to face recognition. In addition, the work is beneficial to practitioners in industry who are working on biometrics-related problems. The prerequisites for optimal use are the basic knowledge of pattern recognition, machine learning, probability theory, and linear algebra.

Imagine a world where machines can see and understand the world the way humans do. Rapid progress in artificial intelligence has led to smartphones that recognize faces, cars that detect pedestrians, and algorithms that suggest diagnoses from clinical images, among many other applications. The success of computer vision is founded on a deep understanding of the neural circuits in the brain responsible for visual processing. This book introduces the neuroscientific study of neuronal computations in visual cortex alongside of the psychological understanding of visual cognition and the burgeoning field of biologically-inspired artificial intelligence. Topics include the neurophysiological investigation of visual cortex, visual illusions, visual disorders, deep convolutional neural networks, machine learning, and generative adversarial networks among others. It is an ideal resource for students and researchers looking to build bridges across different approaches to studying and developing visual systems.

Imagine a world where machines can see and understand the world the way humans do. Rapid progress in artificial intelligence has led to smartphones that recognize faces, cars that detect pedestrians, and algorithms that suggest diagnoses from clinical images, among many other applications. The success of computer vision is founded on a deep understanding of the neural circuits in the brain responsible for visual processing. This book introduces the neuroscientific study of neuronal computations in visual cortex alongside of the psychological understanding of visual cognition and the burgeoning field of biologically-inspired artificial intelligence. Topics include the neurophysiological investigation of visual cortex, visual illusions, visual disorders, deep convolutional neural networks, machine learning, and generative adversarial networks among others. It is an ideal resource for students and researchers looking to build bridges across different approaches to studying and developing visual systems.

Digital Image Processing with C++ presents the theory of digital image processing, and implementations of algorithms using a dedicated library. Processing a digital image means transforming its content (denoising, stylizing, etc.), or extracting information to solve a given problem (object recognition, measurement, motion estimation, etc.). This book presents the mathematical theories underlying digital image processing, as well as their practical implementation through examples of algorithms implemented in the C++ language, using the free and easy-to-use CImg library. Chapters cover in a broad way the field of digital image processing and proposes practical and functional implementations of each method theoretically described. The main topics covered include filtering in spatial and frequency domains, mathematical morphology, feature extraction and applications to segmentation, motion estimation, multispectral image processing and 3D visualization. Students or developers wishing to discover or specialize in this discipline, teachers and researchers wishing to quickly prototype new algorithms, or develop courses, will all find in this book material to discover image processing or deepen their knowledge in this field.

'Phase-only Fresnel holograms,' which can be displayed on a single SLM without the need for lenses or complicated optical accessories, substantially simplifies 3-D holographic display systems. Exploring essential concepts, theories, and formulations of these phase-only Fresnel holograms, this book provides comprehensive coverage of modern methods for generating such holograms, which pave the way for commercial products such as compact holographic projectors, heads-up displays, and data security enhancement. Relevant MATLAB codes are provided for readers to implement and evaluate the theories and formulations of different methods, and can be used as a quick start framework for further research and development. This is a crucial and up-to-date treatment of phase-only Fresnel holograms for students and researchers in electrical and electronic engineering, computer science/engineering, applied physics, information technology, and multimedia technology, as well as engineers and scientists in industry developing new products on 3-D displays and holographic projection.

This book is aimed at those using colour image processing or researching new applications or techniques of colour image processing. It has been clear for some time that there is a need for a text dedicated to colour. We foresee a great increase in the use of colour over the coming years, both in research and in industrial and commercial applications. We are sure this book will prove a useful reference text on the subject for practicing engineers and scientists, for researchers, and for students at doctoral and, perhaps masters, level. It is not intended as an introductory text on image processing, rather it assumes that the reader is already familiar with basic image processing concepts such as image representation in digital form, linear and non-linear filtering, trans forms, edge detection and segmentation, and so on, and has some experience with using, at the least, monochrome equipment. There are many books cov ering these topics and some of them are referenced in the text, where appro priate. The book covers a restricted, but nevertheless, a very important, subset of image processing concerned with natural colour (that is colour as per ceived by the human visual system). This is an important field because it shares much technology and basic theory with colour television and video equipment, the market for which is worldwide and very large; and with the growing field of multimedia, including the use of colour images on the Inter net.

Machine Vision Algorithms in Java provides a comprehensive introduction to the algorithms and techniques associated with machine vision systems. The Java programming language is also introduced, with particular reference to its imaging capabilities. The book contains explanations of key machine vision techniques and algorithms, along with the associated Java source code.Special features include: - A complete self-contained treatment of the topics and techniques essential to the understanding and implementation of machine vision.- An introduction to object-oriented programming and to the Java programming language, with particular reference to its imaging capabilities.- Java source code for a wide range of practical image processing and analysis functions.- Readers will be given the opportunity to download a fully functional Java-based visual programming environment for machine vision, available via the WWW. This contains over 200 image processing, manipulation and analysis functions and will enable users to implement many of the ideas covered in this book. - Details relating to the design of a Java-based visual programming environment for machine vision.- An introduction to the Java 2D imaging and Java Advanced Imaging (JAI) APIs- A wide range of illustrative examples.- Practical treatment of the subject matter. This book is aimed at senior undergraduate and postgraduate students in engineering and computer science as well as practitioners in machine vision who may wish to update or expand their knowledge of the subject. The techniques and algorithms of machine vision are expounded in a way that will be understood not only by specialists but also by those who are less familiar with the topic.

This book is dedicated for engineers and researchers who would like to increase the knowledge in area of mobile mapping systems. Therefore, the flow of the derived information is divided into subproblems corresponding to certain mobile mapping data and related observations' equations. The proposed methodology is not fulfilling all SLAM aspects evident in the literature, but it is based on the experience within the context of the pragmatic and realistic applications. Thus, it can be supportive information for those who are familiar with SLAM and would like to have broader overview in the subject. The novelty is a complete and interdisciplinary methodology for large-scale mobile mapping applications. The contribution is a set of programming examples available as supportive complementary material for this book. All observation equations are implemented, and for each, the programming example is provided. The programming examples are simple C++ implementations that can be elaborated by students or engineers; therefore, the experience in coding is not mandatory. Moreover, since the implementation does not require many additional external programming libraries, it can be easily integrated with any mobile mapping framework. Finally, the purpose of this book is to collect all necessary observation equations and solvers to build computational system capable providing large-scale maps.

This book includes best selected, high-quality research papers presented at the International Conference on Intelligent Manufacturing and Energy Sustainability (ICIMES 2021) held at the Department of Mechanical Engineering, Malla Reddy College of Engineering & Technology (MRCET), Maisammaguda, Hyderabad, India, during June 18-19, 2021. It covers topics in the areas of automation, manufacturing technology and energy sustainability and also includes original works in the intelligent systems, manufacturing, mechanical, electrical, aeronautical, materials, automobile, bioenergy and energy sustainability.

Single and Multi-Objective Evolutionary Computation (MOEA), Genetic Algorithms (GAs), Artificial Neural Networks (ANNs), Fuzzy Controllers (FCs), Particle Swarm Optimization (PSO) and Ant colony Optimization (ACO) are becoming omnipresent in almost every intelligent system design. Unfortunately, the application of the majority of these techniques is complex and so requires a huge computational effort to yield useful and practical results. Therefore, dedicated hardware for evolutionary, neural and fuzzy computation is a key issue for designers. With the spread of reconfigurable hardware such as FPGAs, digital as well as analog hardware implementations of such computation become cost-effective.The idea behind this book is to offer a variety of hardware designs for soft computing techniques that can be embedded in any final product. Also, to introduce the successful application of soft computing technique to solve many hard problems encountered during the design of embedded hardware designs. Reconfigurable embedded designs for GAs, ANNs, FCs and PSO are presented and evaluated. Also, the application of quantum-based evolutionary computation and multi-objective evolutionary computation as well as ACO are applied to solve hard problems related to circuit synthesis, IP assignment, mapping and routing of applications on Network-On-Chip infrastructures."

The sixth edition has been revised and extended. The whole textbook is now clearly partitioned into basic and advanced material in order to cope with the ever-increasing field of digital image processing. In this way, you can first work your way through the basic principles of digital image processing without getting overwhelmed by the wealth of the material and then extend your studies to selected topics of interest. Each chapter now includes exercises that help you to test your understanding, train your skills, and introduce you to real-world image processing tasks. An important part of the exercises is a wealth of interactive computer exercises, which cover all topics of this textbook. These exercises are performed with the image processing software heurisko, which is included on the accompanying CD-ROM. In this way you can get own practical experience with almost all topics and algorithms covered by this book. The complete hyperlinked text of the book is now available on the accompanying CD-ROM.

This book focuses on the latest developments in the fields of visual AI, image processing and computer vision. It shows research in basic techniques like image pre-processing, feature extraction, and enhancement, along with applications in biometrics, healthcare, neuroscience and forensics. The book highlights algorithms, processes, novel architectures and results underlying machine intelligence with detailed execution flow of models.

This book serves as the first guideline of the integrative approach, optimal for our new and young generations. Recent technology advancements in computer vision, IoT sensors, and analytics open the door to highly impactful innovations and applications as a result of effective and efficient integration of those. Such integration has brought to scientists and engineers a new approach -the integrative approach. This offers far more rapid development and scalable architecting when comparing to the traditional hardcore developmental approach. Featuring biomedical and healthcare challenges including COVID-19, we present a collection of carefully selective cases with significant added- values as a result of integrations, e.g., sensing with AI, analytics with different data sources, and comprehensive monitoring with many different sensors, while sustaining its readability.

This book features selected papers presented at the 3rd International Conference on Recent Innovations in Computing (ICRIC 2020), held on 20-21 March 2020 at the Central University of Jammu, India, and organized by the university's Department of Computer Science & Information Technology. It includes the latest research in the areas of software engineering, cloud computing, computer networks and Internet technologies, artificial intelligence, information security, database and distributed computing, and digital India.

This second edition of G. Winkler's successful book on random field approaches to image analysis, related Markov Chain Monte Carlo methods, and statistical inference with emphasis on Bayesian image analysis concentrates more on general principles and models and less on details of concrete applications. Addressed to students and scientists from mathematics, statistics, physics, engineering, and computer science, it will serve as an introduction to the mathematical aspects rather than a survey. Basically no prior knowledge of mathematics or statistics is required.The second edition is in many parts completely rewritten and improved, and most figures are new. The topics of exact sampling and global optimization of likelihood functions have been added. This second edition comes with a CD-ROM by F. Friedrich,containing a host of (live) illustrations for each chapter. In an interactive environment, readers can perform their own experiments to consolidate the subject.

The idea for this text emerged over several years as the authors participated in research projects related to analysis of data from NASA's RHESSI Small Explorer mission. The data produced over the operational lifetime of this mission inspired many investigations related to a specific science question: the when, where, and how of electron acceleration during solar flares in the stressed magnetic environment of the active Sun. A vital key to unlocking this science problem is the ability to produce high-quality images of hard X-rays produced by bremsstrahlung radiation from electrons accelerated during a solar flare. The only practical way to do this within the technological and budgetary limitations of the RHESSI era was to opt for indirect modalities in which imaging information is encoded as a set of two-dimensional spatial Fourier components. Radio astronomers had employed Fourier imaging for many years. However, differently than for radio astronomy, X-ray images produced by RHESSI had to be constructed from a very limited number of sparsely distributed and very noisy Fourier components. Further, Fourier imaging is hardly intuitive, and extensive validation of the methods was necessary to ensure that they produced images with sufficient accuracy and fidelity for scientific applications. This book summarizes the results of this development of imaging techniques specifically designed for this form of data. It covers a set of published works that span over two decades, during which various imaging methods were introduced, validated, and applied to observations. Also considering that a new Fourier-based telescope, STIX, is now entering its nominal phase on-board the ESA Solar Orbiter, it became more and more apparent to the authors that it would be a good idea to put together a compendium of these imaging methods and their applications. Hence the book you are now reading.

This book constitutes the refereed post-conference proceedings of the First IFIP TC 5 International Conference on Computer Science Protecting Human Society Against Epidemics, ANTICOVID 2021, held virtually in June 2021.The 7 full and 4 short papers presented were carefully reviewed and selected from 20 submissions. The papers are concerned with a very large spectrum of problems, ranging from linguistics for automatic translation of medical terms, to a proposition for a worldwide system of fast reaction to emerging pandemic.

The material presented in this book originates from the first Eurographics Workshop on Graphics Hardware, held in Lisbon, Portugal, in August 1986. Leading experts in the field present the state of their ongoing graphics hardware projects and give their individual views of future developments. The final versions of the contributions are written in the light of in-depth discussions at the workshop. The book starts a series of EurographicSeminars volumes on the state-of-the-art of graphics hardware. This volume presents a unique collection of material which covers the following topics: workstation architectures, traffic simulators, hardware support for geometric modeling, ray-tracing. It therefore will be of interest to all computer graphics professionals wishing to gain a deeper knowledge of graphics hardware.

Offering a practical alternative to the conventional methods used in signal processing applications, this book discloses numerical techniques and explains how to evaluate the frequency-domain attributes of a waveform without resorting to actual transformation through Fourier methods. This book should prove of interest to practitioners in any field who may require the analysis, association, recognition or processing of signals, and undergraduate students of signal processing.

Markov models are extremely useful as a general, widely applicable tool for many areas in statistical pattern recognition.

This unique text/reference places the formalism of Markov chain and hidden Markov models at the very center of its examination of current pattern recognition systems, demonstrating how the models can be used in a range of different applications. Thoroughly revised and expanded, this new edition now includes a more detailed treatment of the EM algorithm, a description of an efficient approximate Viterbi-training procedure, a theoretical derivation of the perplexity measure, and coverage of multi-pass decoding based on "n"-best search. Supporting the discussion of the theoretical foundations of Markov modeling, special emphasis is also placed on practical algorithmic solutions.

Topics and features: introduces the formal framework for Markov models, describing hidden Markov models and Markov chain models, also known as n-gram models; covers the robust handling of probability quantities, which are omnipresent when dealing with these statistical methods; presents methods for the configuration of hidden Markov models for specific application areas, explaining the estimation of the model parameters; describes important methods for efficient processing of Markov models, and the adaptation of the models to different tasks; examines algorithms for searching within the complex solution spaces that result from the joint application of Markov chain and hidden Markov models; reviews key applications of Markov models in automatic speech recognition, character and handwriting recognition, and the analysis of biological sequences.

Researchers, practitioners, and graduate students of pattern recognition will all find this book to be invaluable in aiding their understanding of the application of statistical methods in this area.

This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging. Also there will be a special track on computer-aided diagnosis on COVID-19 by CT and X-rays images. Given its coverage, the book provides both a forum and valuable resource for researchers involved in image formation, experimental methods, image performance, segmentation, pattern recognition, feature extraction, classifier design, machine learning / deep learning, radiomics, CAD workstation design, human-computer interaction, databases, and performance evaluation.

Computational geometry as an area of research in its own right emerged in the early seventies of this century. Right from the beginning, it was obvious that strong connections of various kinds exist to questions studied in the considerably older field of combinatorial geometry. For example, the combinatorial structure of a geometric problem usually decides which algorithmic method solves the problem most efficiently. Furthermore, the analysis of an algorithm often requires a great deal of combinatorial knowledge. As it turns out, however, the connection between the two research areas commonly referred to as computa tional geometry and combinatorial geometry is not as lop-sided as it appears. Indeed, the interest in computational issues in geometry gives a new and con structive direction to the combinatorial study of geometry. It is the intention of this book to demonstrate that computational and com binatorial investigations in geometry are doomed to profit from each other. To reach this goal, I designed this book to consist of three parts, acorn binatorial part, a computational part, and one that presents applications of the results of the first two parts. The choice of the topics covered in this book was guided by my attempt to describe the most fundamental algorithms in computational geometry that have an interesting combinatorial structure. In this early stage geometric transforms played an important role as they reveal connections between seemingly unrelated problems and thus help to structure the field."

A long long time ago, echoing philosophical and aesthetic principles that existed since antiquity, William of Ockham enounced the principle of parsimony, better known today as Ockham's razor: "Entities should not be multiplied without neces sity. " This principle enabled scientists to select the "best" physical laws and theories to explain the workings of the Universe and continued to guide scienti?c research, leadingtobeautifulresultsliketheminimaldescriptionlength approachtostatistical inference and the related Kolmogorov complexity approach to pattern recognition. However, notions of complexity and description length are subjective concepts anddependonthelanguage"spoken"whenpresentingideasandresults. The?eldof sparse representations, that recently underwent a Big Bang like expansion, explic itly deals with the Yin Yang interplay between the parsimony of descriptions and the "language" or "dictionary" used in them, and it became an extremely exciting area of investigation. It already yielded a rich crop of mathematically pleasing, deep and beautiful results that quickly translated into a wealth of practical engineering applications. You are holding in your hands the ?rst guide book to Sparseland, and I am sure you'll ?nd in it both familiar and new landscapes to see and admire, as well as ex cellent pointers that will help you ?nd further valuable treasures. Enjoy the journey to Sparseland! Haifa, Israel, December 2009 Alfred M. Bruckstein vii Preface This book was originally written to serve as the material for an advanced one semester (fourteen 2 hour lectures) graduate course for engineering students at the Technion, Israel.