Digital image processing is a field that is constantly improving. Gaining high-level understanding from digital images is a key requirement for computing. One aspect of study that is assisting with this advancement is fractal theory. This new science has gained momentum and popularity as it has become a key topic of research in the area of image analysis. Examining Fractal Image Processing and Analysis is an essential reference source that discusses fractal theory applications and analysis, including box-counting analysis, multi-fractal analysis, 3D fractal analysis, and chaos theory, as well as recent trends in other soft computing techniques. Featuring research on topics such as image compression, pattern matching, and artificial neural networks, this book is ideally designed for system engineers, computer engineers, professionals, academicians, researchers, and students seeking coverage on problem-oriented processing techniques and imaging technologies.

Interfaces within computers, computing, and programming are consistently evolving and continue to be relevant to computer science as it progresses. Advancements in human-computer interactions, their aesthetic appeal, ease of use, and learnability are made possible due to the creation of user interfaces and result in further growth in science, aesthetics, and practical applications. Interface Support for Creativity, Productivity, and Expression in Computer Graphics is a collection of innovative research on usability, the apps humans use, and their sensory environment. While highlighting topics such as image datasets, augmented reality, and visual storytelling, this book is ideally designed for researchers, academicians, graphic designers, programmers, software developers, educators, multimedia specialists, and students seeking current research on uniting digital content with the physicality of the device through applications, thus addressing sensory perception.

For junior- to graduate-level courses in computer graphics. Assuming no background in computer graphics, this junior- to graduate-level textbook presents basic principles for the design, use, and understanding of computer graphics systems and applications. The authors, authorities in their field, offer an integrated approach to two-dimensional and three-dimensional graphics topics. A comprehensive explanation of the popular OpenGL programming package, along with C++ programming examples illustrates applications of the various functions in the OpenGL basic library and the related GLU and GLUT packages.

Image and Video Processing is an active area of research due to its potential applications for solving real-world problems. Integrating computational intelligence to analyze and interpret information from image and video technologies is an essential step to processing and applying multimedia data. Emerging Technologies in Intelligent Applications for Image and Video Processing presents the most current research relating to multimedia technologies including video and image restoration and enhancement as well as algorithms used for image and video compression, indexing and retrieval processes, and security concerns. Featuring insight from researchers from around the world, this publication is designed for use by engineers, IT specialists, researchers, and graduate level students.

Security video surveillance systems, such as homeland security and national defence, rely on specific mathematical algorithms in order to run effectively. It is essential for these parameters to be understood in order to design and create a successful system. Video Surveillance Techniques and Technologies presents empirical research and acquired experience on the original solutions and mathematical algorithms for motion detection and object identification problems. Emphasising a wide variety of applications of security systems, this book is an essential tool for graduate students and professionals in the field of signal and image processing applied in static/moving object detection, tracking, and identification.

Deep learning has become a trending area of research due to its adaptive characteristics and high levels of applicability. In recent years, researchers have begun applying deep learning strategies to image analysis and pattern recognition for solving technical issues within image classification. As these technologies continue to advance, professionals have begun translating this intelligent programming language into mobile applications for devices. Programmers and web developers are in need of significant research on how to successfully develop pattern recognition applications using intelligent programming. MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities is an essential reference source that presents a solution to developing intelligent pattern recognition Apps on iOS devices based on MatConvNet deep learning. Featuring research on topics such as medical image diagnosis, convolutional neural networks, and character classification, this book is ideally designed for programmers, developers, researchers, practitioners, engineers, academicians, students, scientists, and educators seeking coverage on the specific development of iOS mobile applications using pattern recognition strategies.

This book equips readers to understand a complex range of healthcare products that are used to diagnose, monitor, and treat diseases or medical conditions affecting humans. The first part of the book presents medical technologies such as medical information retrieval, tissue engineering techniques, 3D medical imaging, nanotechnology innovations in medicine, medical wireless sensor networks, and knowledge mining techniques in medicine. The second half of the book focuses on healthcare technologies including prediction hospital readmission risk, modeling e-health framework, personal Web in healthcare, security issues for medical records, and personalized services in healthcare. The contributors are leading world researchers who share their innovations, making this handbook the definitive resource on these topics. Handbook of Medical and Healthcare Technologies is intended for a wide audience including academicians, designers, developers, researchers and advanced-level students. It is also valuable for business managers, entrepreneurs, and investors within the medical and healthcare industries.

* Written and structured in relation to media psychology courses and curricula and includes a range of pedagogical features to aid teaching and student learning including vocabulary and key terms, discussion questions, and boxed examples * Offers an up to date perspective by engaging with digital media and cyberpsychology, as well as topical issues such as fake news, positive psychology, gaming, online dating, and social media * Written by a team of expert authors to offer a comprehensive overview of the topic are that is suitable for undergraduate and introductory master's course around the world

* Written and structured in relation to media psychology courses and curricula and includes a range of pedagogical features to aid teaching and student learning including vocabulary and key terms, discussion questions, and boxed examples * Offers an up to date perspective by engaging with digital media and cyberpsychology, as well as topical issues such as fake news, positive psychology, gaming, online dating, and social media * Written by a team of expert authors to offer a comprehensive overview of the topic are that is suitable for undergraduate and introductory master's course around the world

This book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding. With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloud processing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods. A major portion of this book focuses on explainable machine learning as a different approach to deep learning. The explainable machine learning methods offer a series of advantages over traditional methods and deep learning methods. This is a main highlight and novelty of the book. By tackling three research tasks -- 3D object recognition, segmentation, and registration using our methodology -- readers will have a sense of how to solve problems in a different way and can apply the frameworks to other 3D computer vision tasks, thus give them inspiration for their own future research. Numerous experiments, analysis and comparisons on three 3D computer vision tasks (object recognition, segmentation, detection and registration) are provided so that readers can learn how to solve difficult Computer Vision problems.

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 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.

This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem-oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.

With the advancement of technology in the modern world, the constant influx of data, information, and computing can become droning and one-dimensional. Re-examining these methods through a different approach helps highlight broader perspectives and further understanding. Applying abstract and holistic methods, such as nature and visualization, to computing technologies is a developing area of study but has yet to be empirically researched. Graphical Thinking for Science and Technology Through Knowledge Visualization provides emerging research exploring the theoretical and practical aspects of implementing visuals and images within data and information. The text contains projects, examples of students' solutions, and invites the reader to apply graphical thinking. Featuring coverage on a broad range of topics such as nanoscale structures, computer graphics, and data visualization, this book is ideally designed for software engineers, instructional designers, researchers, scientists, artists, marketers, media professionals, and students seeking current research on applying artistic solutions within information and computing.

This book takes a hemispheric approach to contemporary urban intervention, examining urban ecologies, communication technologies, and cultural practices in the twenty-first century. It argues that governmental and social regimes of control and forms of political resistance converge in speculation on disaster and that this convergence has formed a vision of urban environments in the Americas in which forms of play and imaginations of catastrophe intersect in the vertical field. Schifani explores a diverse range of resistant urban interventions, imagining the city as on the verge of or enmeshed in catastrophe. She also presents a model of ecocriticism that addresses aesthetic practices and forms of play in the urban environment. Tracing the historical roots of such tactics as well as mapping their hopes for the future will help the reader to locate the impacts of climate change not only on the physical space of the city, but also on the epistemological and aesthetic strategies that cities can help to engender. This book will be of great interest to students and scholars of Urban Studies, Media Studies, American Studies, Global Studies, and the broad and interdisciplinary field of Environmental Humanities.

This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI 2019), held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), which took place in Shenzhen, China on October 17, 2019. This book presents the latest advances in the rapidly expanding field of diffusion MRI. It shares new perspectives on the latest research challenges for those currently working in the field, but also offers a valuable starting point for anyone interested in learning about computational techniques in diffusion MRI. The book includes rigorous mathematical derivations, a wealth of rich, full-colour visualisations and extensive clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics. Readers will find contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in vivo recovery of microstructural and connectivity features, as well as diffusion-relaxometry and frontline applications in research and clinical practice. This edition includes invited works from high-profile researchers with a specific focus on three new and important topics that are gaining momentum within the diffusion MRI community, including diffusion MRI signal acquisition and processing strategies, machine learning for diffusion MRI, and diffusion MRI outside the brain and clinical applications.

This book is a collection of selected papers presented at the First Congress on Intelligent Systems (CIS 2020), held in New Delhi, India, during September 5-6, 2020. It includes novel and innovative work from experts, practitioners, scientists, and decision-makers from academia and industry. It covers selected papers in the area of computer vision. This book covers new tools and technologies in some of the important areas of medical science like histopathological image analysis, cancer taxonomy, use of deep learning architecture in dental care, and many more. Furthermore, this book reviews and discusses the use of intelligent learning-based algorithms for increasing the productivity in agricultural domain.

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. 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.

This publication contributes to the serious games field by investigating original contributions and methods that use serious games in various domains. This comprehensive and timely publication works as an essential reference source, building on the available literature in the field of Serious Games for the economic and social development of countries while providing for further research opportunities in this dynamic and growing field. Thus, the book provides the opportunity for a reflection on this important issue, increasing the understanding of the importance of Serious Games in the context of organizations' improvements, providing relevant academic work, empirical research findings, and an overview of this relevant field of study. This text provides the resources necessary for policy makers, technology developers and managers to adopt and implement solutions for a more digital era.

This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field. In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.

Inverse problems such as imaging or parameter identification deal with the recovery of unknown quantities from indirect observations, connected via a model describing the underlying context. While traditionally inverse problems are formulated and investigated in a static setting, we observe a significant increase of interest in time-dependence in a growing number of important applications over the last few years. Here, time-dependence affects a) the unknown function to be recovered and / or b) the observed data and / or c) the underlying process. Challenging applications in the field of imaging and parameter identification are techniques such as photoacoustic tomography, elastography, dynamic computerized or emission tomography, dynamic magnetic resonance imaging, super-resolution in image sequences and videos, health monitoring of elastic structures, optical flow problems or magnetic particle imaging to name only a few. Such problems demand for innovation concerning their mathematical description and analysis as well as computational approaches for their solution.