Gamification is being used everywhere; despite its apparent plethora of benefits, the unbalanced use of its main mechanics can end up in catastrophic results for a company or institution. Currently, there is a lack of knowledge of what it is, leading to its unregulated and ad hoc use without any prior planning. This unbalanced use prejudices the achievement of the initial goals and impairs the user's evolution, bringing potential negative reflections. Currently, there are few specifications and modeling languages that allow the creation of a system of rules to serve as the basis for a gamification engine. Consequently, programmers implement gamification in a variety of ways, undermining any attempt at reuse and negatively affecting interoperability. Next-Generation Applications and Implementations of Gamification Systems synthesizes all the trends, best practices, methodologies, languages, and tools that are used to implement gamification. It also discusses how to put gamification in action by linking academic and informatics researchers with professionals who use gamification in their daily work to disseminate and exchange the knowledge, information, and technology provided by the international communities in the area of gamification throughout the 21st century. Covering topics such as applied and cloud gamification, chatbots, deep learning, and certifications and frameworks, this book is ideal for programmers, computer scientists, software engineers, practitioners of technological companies, managers, academicians, researchers, and students.

As technology continues to develop, the healthcare industry must adapt and implement new technologies and services. Recent advancements, opportunities, and challenges for bio-medical image processing and authentication in telemedicine must be considered to ensure patients receive the best possible care. Advancements in Bio-Medical Image Processing and Authentication in Telemedicine introduces recent advancements, opportunities, and challenges for bio-medical image processing and authentication in telemedicine and discusses the design of high-accuracy decision support systems. Covering key topics such as artificial intelligence, medical imaging, telemedicine, and technology, this premier reference source is ideal for medical professionals, nurses, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

Throughout the 1990s, artists experimented with game engine technologies to disrupt our habitual relationships to video games. They hacked, glitched, and dismantled popular first-person shooters such as Doom (1993) and Quake (1996) to engage players in new kinds of embodied activity. In Unstable Aesthetics: Game Engines and the Strangeness of Art Modding, Eddie Lohmeyer investigates historical episodes of art modding practices-the alteration of a game system's existing code or hardware to generate abstract spaces-situated around a recent archaeology of the game engine: software for rendering two and three-dimensional gameworlds. The contemporary artists highlighted throughout this book-Cory Arcangel, JODI, Julian Oliver, Krista Hoefle, and Brent Watanabe, among others -- were attracted to the architectures of engines because they allowed them to explore vital relationships among abstraction, technology, and the body. Artists employed a range of modding techniques-hacking the ROM chips on Nintendo cartridges to produce experimental video, deconstructing source code to generate psychedelic glitch patterns, and collaging together surreal gameworlds-to intentionally dissect the engine's operations and unveil illusions of movement within algorithmic spaces. Through key moments in game engine history, Lohmeyer formulates a rich phenomenology of video games by focusing on the liminal spaces of interaction among system and body, or rather the strangeness of art modding.

This textbook guides readers through their first steps into the challenging world of mimicking human vision with computational tools and techniques pertaining to the field of image processing and analysis. While today's theoretical and applied processing and analysis of images meet with challenging and complex problems, this primer is confined to a much simpler, albeit critical, collection of image-to-image transformations, including image normalisation, enhancement, and filtering.It serves as an introduction to beginners, a refresher for undergraduate and graduate students, as well as engineers and computer scientists confronted with a problem to solve in computer vision. The book covers basic image processing/computer vision pipeline techniques, which are widely used in today's computer vision, computer graphics, and image processing, giving the readers enough knowledge to successfully tackle a wide range of applied problems.

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.

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention.

Image data has portrayed immense potential as a foundation of information for numerous applications. Recent trends in multimedia computing have witnessed a rapid growth in digital image collections, resulting in a need for increased image data management. Feature Dimension Reduction for Content-Based Image Identification is a pivotal reference source that explores the contemporary trends and techniques of content-based image recognition. Including research covering topics such as feature extraction, fusion techniques, and image segmentation, this book explores different theories to facilitate timely identification of image data and managing, archiving, maintaining, and extracting information. This book is ideally designed for engineers, IT specialists, researchers, academicians, and graduate-level students seeking interdisciplinary research on image processing and analysis.

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.

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.

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 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.