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Activity, Behavior, and Healthcare Computing relates to the fields of vision and sensor-based human action or activity and behavior analysis and recognition. As well as a series of methodologies, the book includes original methods, exploration of new applications, excellent survey papers, presentations on relevant datasets, challenging applications, ideas and future scopes with guidelines. Featuring contributions from top experts and top research groups globally related to this domain, the book covers action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, healthcare, dementia, nursing, Parkinson's disease, and related areas. It addresses various challenges and aspects of human activity recognition – both in sensor-based and vision-based domains. This is a unique edited book covering both domains in the field of activity and behavior.

Biomedical engineering is a rapidly growing interdisciplinary area that is providing solutions to biological and medical problems and improving the healthcare system. It is connected to various applications like protein structure prediction, computer-aided drug design, and computerized medical diagnosis based on image and signal data, which accomplish low-cost, accurate, and reliable solutions for improving healthcare services. With the recent advancements, machine learning (ML) and deep learning (DL) techniques are widely used in biomedical engineering to develop intelligent decision-making healthcare systems in real-time. However, accuracy and reliability in model performance can be a concern in tackling data generated from medical images and signals, making it challenging for researchers and practitioners. Therefore, optimized models can produce quality healthcare services to handle the complexities involved in biomedical research. Various optimization techniques have been employed to optimize parameters, hyper-parameters, and architectural information of ML/DL models explicitly applied to biological, medical, and signal data. The swarm intelligence approach has the potential to solve complex non-linear optimization problems. It mimics the collective behavior of social swarms such as ant colonies, honey bees, and bird flocks. The cooperative nature of swarms can search global settings of ML/DL models, which efficiently provide the solution to biomedical engineering applications. Finally, the book aims to provide the utility of swarm optimization and similar optimization techniques to design ML/DL models to improve the solutions related to biomedical engineering.

Applying Color Theory to Digital Media and Visualization provides an overview of the application of color theory concepts to digital media and visualization. It highlights specific color concepts such as color harmony and data color schemes. Examples of generative AI solutions for color scheme suggestion are provided. The usage of these concepts is shown with actual online and mobile tools. Color deficiencies are reviewed, and color tools for examining how a specific color map design will look to someone with the deficiency are discussed. A five-stage colorization process is defined and applied to case study examples.Features: Presents color theory and data color concepts that can be applied to digital media and visualization problems over and over again Offers a comprehensive review of the historical progression of color models Demonstrates actual case study implementations of color analyses tools Provides overview of color theory and harmony analytics in terms of online and mobile analysis tools Teaches the color theory language to use in interacting with color management professionals Unlike many books on color, which examine artists'use of color, color management or color science, this book applies fundamental color concepts to digital media and visualization solutions, and the new edition includes generative AI solutions for color suggestion. A video summary of the chapters by the author can also be found here: https://www.youtube.com/watch?v=aGDXyTd1UWk. This is the ideal book for digital media and visualization content creators and developers.

The major goals of texture research in computer vision are to understand, model, and process texture and, ultimately, to simulate the human visual learning process using computer technologies. In the last decade, artificial intelligence has been revolutionized by machine learning and big data approaches, outperforming human prediction on a wide range of problems. In particular, deep learning convolutional neural networks (CNNs) are particularly well suited to texture analysis. This volume presents important branches of texture analysis methods which find a proper application in AI-based medical image analysis. This book: Discusses first-order, second-order statistical methods, local binary pattern (LBP) methods, and filter bank-based methods Covers spatial frequency-based methods, Fourier analysis, Markov random fields, Gabor filters, and Hough transformation Describes advanced textural methods based on DL as well as BD and advanced applications of texture to medial image segmentation Is aimed at researchers, academics, and advanced students in biomedical engineering, image analysis, cognitive science, and computer science and engineering This is an essential reference for those looking to advance their understanding in this applied and emergent field.

The major goals of texture research in computer vision are to understand, model, and process texture, and ultimately, to simulate the human visual learning process using computer technologies. In the last decade, artificial intelligence has been revolutionized by machine learning and big data approaches, outperforming human prediction on a wide range of problems. In particular, deep learning convolutional neural networks (CNNs) are particularly well suited to texture analysis. This book examines four major application domains related to texture analysis and their relationship to AI-based industrial applications: texture classification, texture segmentation, shape from texture, and texture synthesis. This volume: Discusses texture-based segmentation for extracting image shape features, modeling and segmentation of noisy and textured images, spatially constrained color-texture model for image segmentation, and texture segmentation using Gabor filters Examines textural features for image classification, a statistical approach for classification, texture classification from random features, and applications of texture classifications Describes shape from texture, including general principles, 3D shapes, and equations for recovering shape from texture Surveys texture modeling, including extraction based on Hough transformation and cycle detection, image quilting, gray level run lengths, and use of Markov random fields Aimed at researchers, academics, and advanced students in biomedical engineering, image analysis, cognitive science, and computer science and engineering, this is an essential reference for those looking to advance their understanding in this applied and emergent field.

This book delves into the history of biometrics, the different systems that have been developed to date, problems that have arisen from these systems, the necessity of AI-based biometrics systems, different AI techniques developed to date (including machine learning, deep learning, natural language processing, and pattern recognition), their potential uses and applications, security and privacy issues in AI-based Biometric systems, current trends in AI-based biometrics, and presents case studies of AI-based biometrics.

Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing.Divided into two volumes, Volume Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos.Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts.Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing Applications is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.

Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing.Divided into two volumes, Volume One begins by introducing the fundamental concepts of deep learning, providing readers with a solid foundation to understand its relevance in multimedia processing. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos.Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts.Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing Applications is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.

Human Activity and Behavior Analysis relates to the field of vision and sensor-based human action or activity and behavior analysis and recognition. The book includes a series of methodologies, surveys, relevant datasets, challenging applications, ideas, and future prospects.The book discusses topics such as action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, and related areas. This volume focuses on two main subject areas: Movement and Sensors, and Sports Activity Analysis.The editors are experts in these arenas, and the contributing authors are drawn from high-impact research groups around the world. This book will be of great interest to academics, students, and professionals working and researching in the field of human activity and behavior analysis.

Human Activity and Behavior Analysis relates to the field of vision and sensor-based human action or activity and behavior analysis and recognition. The book includes a series of methodologies, surveys, relevant datasets, challenging applications, ideas, and future prospects.The book discusses topics such as action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, and related areas. This volume focuses on relevant activities in three main subject areas: Healthcare and Emotion, Mental Health, and Nurse Care Records.The editors are experts in these arenas and the contributing authors are drawn from high-impact research groups around the world. This book will be of great interest to academics, students, and professionals working and researching in the field of human activity and behavior analysis.

This book is devoted to the new standards, technologies, and communication systems for Artificial Intelligence of Things (AIoT) networks. Smart and intelligent communication networks have gained significant attention due to the combination of AI and IoT networks to improve human and machine interfaces and enhance data processing and services. AIoT networks involve the collection of data from several devices and sensor nodes in the environment. AI can enhance these networks to make them faster, greener, smarter, and safer. Computer vision, language processing, and speech recognition are some examples of AIoT networks.Due to a large number of devices in today's world, efficient and intelligent data processing is essential for problem-solving and decision-making. AI multiplies the value of these networks and promotes intelligence and learning capabilities, especially in homes, offices, and cities. However, several challenges have been observed in deploying AIoT networks, such as scalability, complexity, accuracy, and robustness. In addition, these networks are integrated with cloud, 5G networks, and blockchain methods for service provision. Many different solutions have been proposed to address issues related to machine and deep learning methods, ontology-based approaches, genetic algorithms, and fuzzy-based systems.This book aims to contribute to the state of the art and present current standards, technologies, and approaches for AIoT networks. This book focuses on existing solutions in AIoT network technologies, applications, services, standards, architectures, and security provisions. This book also introduces some new architectures and models for AIoT networks.

This book will highlight cutting-edge research with a particular emphasis on interdisciplinary approaches, novel techniques, and solutions to provide digital image security for applications in diverse areas. It further discusses important topics such as biometric imaging, big data security and privacy in healthcare, security and privacy in the internet of things, and security in cloud-based image processing.This book Presents new ideas, approaches, theories, and practices with a focus on digital image security and privacy solutions for real-world applications. Discusses security in cloud-based image processing for smart city applications. Provides an overview of innovative security techniques that are being developed to ensure the guaranteed authenticity of transmitted, shared, or stored digital images. Highlights approaches such as watermarking, blockchain, and hashing. to secure digital images in artificial intelligence, machine learning, cloud computing, and temper detection environments. Explains important topics such as biometric imaging, blockchain for digital data security, and protection systems against personal identity theft. It will serve as an ideal reference text for senior undergraduate, graduate students, academic researchers, and professionals in the fields including electrical engineering, electronics, communications engineering, and computer engineering.

Written in accessible language without mathematical formulas, this short book provides an overview of the wide and varied applications of artificial intelligence (AI) across the spectrum of physical sciences. Focusing in particular on AI's ability to extract patterns from data, known as machine learning (ML), the book includes a chapter on important machine learning algorithms and their respective applications in physics. It then explores the use of ML across a number of important sub-fields in more detail, ranging from particle, molecular and condensed matter physics, to astrophysics, cosmology and the theory of everything. The book covers such applications as the search for new particles and the detection of gravitational waves from the merging of black holes, and concludes by discussing what the future may hold.

Digital Image Processing with C++: Implementing Reference Algorithms with the CImg Library 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 the field of digital image processing in a broad way and propose 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 and teachers and researchers hoping 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.

Artificial Intelligence (AI) in general and machine learning (ML) and deep learning (DL) in particular and related digital technologies are a couple of fledging paradigms that next-generation healthcare services are sprinting towards. These digital technologies can transform various aspects of healthcare, leveraging advances in computing and communication power. With a new spectrum of business opportunities, AI-powered healthcare services will improve the lives of patients, their families, and societies. However, the application of AI in the healthcare field requires special attention given the direct implication with human life and well-being. Rapid progress in AI leads to the possibility of exploiting healthcare data for designing practical tools for automated diagnosis of chronic diseases such as dementia and diabetes. This book highlights the current research trends in applying AI models in various disease diagnoses and prognoses to provide enhanced healthcare solutions. The primary audience of the book are postgraduate students and researchers in the broad domain of healthcare technologies. Features In-depth coverage of the role of AI in smart healthcare Research guidelines for AI and data science researchers/practitioners interested in the healthcare sector Comprehensive coverage on security and privacy issues for AI in smart healthcare

This book offers a comprehensive introduction to seven commonly used image understanding techniques in modern information technology. Readers of various levels can find suitable techniques to solve their practical problems and discover the latest development in these specific domains.The techniques covered include camera model and calibration, stereo vision, generalized matching, scene analysis and semantic interpretation, multi-sensor image information fusion, content-based visual information retrieval, and understanding spatial-temporal behavior. The book provides aspects from the essential concepts overview and basic principles to detailed introduction, explanation of the current methods and their practical techniques. It also presents discussions on the research trends and latest results in conjunction with new development of technical methods. This is an excellent read for those who do not have a subject background in image technology but need to use these techniques to complete specific tasks. These essential information will also be useful for their further study in the relevant fields.

On-board image processing systems are used to maximize image data transmission efficiency for large volumes of data gathered by Earth observation satellites. This book explains the methods, mathematical models, and key technologies used for these systems. It introduces the background, basic concepts, and the architecture of on-board image processing, along with on-board detection of the image feature and matching, ground control point identification, on-board geometric correction, calibration, geographic registration, etc.• Describes algorithms and methodologies for on-board image processing with FPGA chips.• Migrates the traditional on-ground computing to on-board operation and the image processing is implemented on-board, not on-ground.• Introduces for the first time many key technologies and methods for on-board image processing.• Emphasizes the recent progress in image processing by using on-board FPGA chips.• Includes case studies from the author's extensive research and experience on the topic.This book gives insights into emerging technologies for on-board processing and will benefit senior undergraduate and graduate students of remote sensing, information technology, computer science and engineering, electronic engineering, and geography, as well as researchers and professionals interested in satellite remote sensing image processing in academia, and governmental and commercial sectors.

An influential scientist in the field of artificial intelligence (AI) explains its fundamental concepts and how it is changing culture and society. A particular form of AI is now embedded in our tech, our infrastructure, and our lives. How did it get there? Where and why should we be concerned? And what should we do now? The Shortcut: Why Intelligent Machines Do Not Think Like Us provides an accessible yet probing exposure of AI in its prevalent form today, proposing a new narrative to connect and make sense of events that have happened in the recent tumultuous past, and enabling us to think soberly about the road ahead. This book is divided into ten carefully crafted and easily digestible chapters. Each chapter grapples with an important question for AI. Ranging from the scientific concepts that underpin the technology to wider implications for society, it develops a unified description using tools from different disciplines and avoiding unnecessary abstractions or words that end with -ism. The book uses real examples wherever possible, introducing the reader to the people who have created some of these technologies and to ideas shaping modern society that originate from the technical side of AI. It contains important practical advice about how we should approach AI in the future without promoting exaggerated hypes or fears. Entertaining and disturbing but always thoughtful, The Shortcut confronts the hidden logic of AI while preserving a space for human dignity. It is essential reading for anyone with an interest in AI, the history of technology, and the history of ideas. General readers will come away much more informed about how AI really works today and what we should do next.

This book examines deep learning-based approaches in the field of cancer diagnostics, as well as pre-processing techniques, which are essential to cancer diagnostics. Topics include introduction to current applications of deep learning in cancer diagnostics, pre-processing of cancer data using deep learning, review of deep learning techniques in oncology, overview of advanced deep learning techniques in cancer diagnostics, prediction of cancer susceptibility using deep learning techniques, prediction of cancer reoccurrence using deep learning techniques, deep learning techniques to predict the grading of human cancer, different human cancer detection using deep learning techniques, prediction of cancer survival using deep learning techniques, complexity in the use of deep learning in cancer diagnostics, and challenges and future scopes of deep learning techniques in oncology.

There is presently a drastic growth in multimedia data. During the Covid-19 pandemic, we observed that images helped doctors immensely in the rapid detection of Covid-19 infection in patients. There are many critical applications in which images play a vital role. These applications use raw image data to extract some useful information about the world around us. The quick extraction of valuable information from raw images is one challenge that academicians and professionals face in the present day. This is where image processing comes into action. Image processing's primary purpose is to get an enhanced image or extract some useful information from raw image data. Therefore, there is a major need for some technique or system that addresses this challenge. Intelligent Systems have emerged as a solution to address quick image information extraction. In simple words, an Intelligent System can be defined as a mathematical model that adapts itself to deal with a problem's dynamicity. These systems learn how to act so an image can reach an objective. An Intelligent System helps accomplish various image-processing functions like enhancement, segmentation, reconstruction, object detection, and morphing. The advent of Intelligent Systems in the image-processing field has leveraged many critical applications for humankind. These critical applications include factory automation, biomedical imaging analysis, decision econometrics, as well as related challenges.

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.

AI Knowledge Transfer from the University to Society: Applications in High-Impact Sectors brings together examples from the'Innovative Ecosystem with Artificial Intelligence for Andalusia 2025'project at the University of Seville, a series of sub-projects composed of research groups and different institutions or companies that explore the use of Artificial Intelligence in a variety of high-impact sectors to lead innovation and assist in decision-making.Key Features Includes chapters on health and social welfare, transportation, digital economy, energy efficiency and sustainability, agro-industry, and tourism Great diversity of authors, expert in varied sectors, belonging to powerful research groups from the University of Seville with proven experience in the transfer of knowledge to the productive sector and agents attached to the Andalucía TECH Campus

This book discusses the use of machine vision and technologies in specific engineering case studies and focuses on how machine vision techniques are impacting every step of industrial processes and how smart sensors and cognitive big data analytics are supporting the automation processes in Industry 4.0 applications. Industry 4.0, the Fourth Industrial Revolution, combines traditional manufacturing with automation and data exchange. Machine vision is used in the industry for reliable product inspections, quality control, and data capture solutions. It combines different technologies to provide important information from the acquisition and analysis of images for robot-based inspection and guidance. Features Presents a comprehensive guide on how to use machine vision for Industry 4.0 applications, such as analysis of images for automated inspections, object detection, object tracking, and more Includes case studies of Robotics Internet of Things with its current and future applications in healthcare, agriculture, and transportation Highlights the inclusion of impaired people in the industry, for example, an intelligent assistant that helps deaf-mute individuals to transmit instructions and warnings in a manufacturing process Examines the significant technological advancements in machine vision for Industrial Internet of Things and explores the commercial benefits using real-world applications from healthcare to transportation Discusses a conceptual framework of machine vision for various industrial applications The book addresses scientific aspects for a wider audience such as senior and junior engineers, undergraduate and postgraduate students, researchers, and anyone interested in the trends, development, and opportunities for machine vision for Industry 4.0 applications.

It seems that artificial intelligence (AI) is always just five years away, but it never arrives. Recently, however, developments have made the practical utility of game theory a genuine reality. Will sport provide the petri dish in which AI will prove itself? What do domain specialists like managers and coaches want to know that they can't currently find out, and can AI provide the answer? What competitive advantages might AI provide for recruitment, performance and tactics, health and fitness, pedagogy, broadcasting, eSports, gambling and stadium design in the future? Written by leading experts in both sports management and AI, AI for Sports begins to answer these and many other questions on the future of AI for sports.

What is artificial intelligence (AI)? What is healthcare robotics? How can AI and healthcare robotics assist in contemporary medicine? Robotics and AI can offer society unimaginable benefits, such as enabling wheelchair users to walk again, performing surgery in a highly automated and minimally invasive way, and delivering care more efficiently. AI for Healthcare Robotics explains what healthcare robots are and how AI empowers them in achieving the goals of contemporary medicine.

Introduction to Visual Effects: A Computational Approach is the first single introduction to the computational and mathematical aspects of visual effects, incorporating both computer vision and graphics. The book also provides the readers with the source code to a library, enabling them to follow the chapters directly and build up a complete visual effects platform. The book covers the basic approaches to camera pose estimation, global illumination, and image-based lighting, and includes chapters on the virtual camera, optimization and computer vision, path tracing and many more.Key features include: Introduction to projective geometry, image-based lighting (IBL), global illumination solved by the Monte Carlo method (Pathtracing), an explanation of a set of optimization methods, and the techniques used for calibrating one, two, and many cameras, including how to use the RANSAC algorithm in order to make the process robust, and providing code to be implemented using the Gnu Scientific Library. C/C++ code using the OpenCV library, to be used in the process of tracking points on a movie (an important step for the matchmove process), and in the construction of modeling tools for visual effects. A simple model of the Bidirectional Reflectance Distribution Function (BRDF) of surfaces and the differential rendering method, allowing the reader to generate consistent shadows, supported by a code that can be used in combination with a software like Luminance HDR.

This reference text offers the reader a comprehensive insight into recent research breakthroughs in blockchain, the Internet of Things (IoT), artificial intelligence and material structure and hybrid technologies in their integrated platform, while also emphasizing their sustainability aspects.The text begins by discussing recent advances in energy materials and energy conversion materials using machine learning, as well as recent advances in optoelectronic materials for solar energy applications. It covers important topics including advancements in electrolyte materials for solid oxide fuel cells, advancements in composite materials for Li-ion batteries, progression of materials for supercapacitor applications, and materials progression for thermochemical storage of low-temperature solar thermal energy systems.This book: Discusses advances in blockchain, the Internet of Things, artificial intelligence, material structure and hybrid technologies Covers intelligent techniques in materials progression for sensor development and energy material characterization using signal processing Examines the integration of phase change materials in construction for thermal energy regulation in new buildings Explores the current happenings in technology in conjunction with basic laws and mathematical models Connecting advances in engineering materials with the use of smart techniques including artificial intelligence, machine learning and Internet of Things (IoT) in a single volume, this text will be especially useful for graduate students, academic researchers and professionals in the fields of electrical engineering, electronics engineering, materials science, mechanical engineering and computer science.

An uncoded multimedia transmission (UMT) system is one that skips quantization and entropy coding in compression and all subsequent binary operations, including channel coding and bit-to-symbol mapping of modulation. By directly transmitting non-binary symbols with amplitude modulation, the uncoded system avoids the annoying cliff effect observed in the coded transmission system. This advantage makes uncoded transmission more suited to both unicast in varying channel conditions and multicast to heterogeneous users.Particularly, in the first part of Uncoded Multimedia Transmission, we consider how to improve the efficiency of uncoded transmission and make it on par with coded transmission. We then address issues and challenges regarding how to better utilize temporal and spatial correlation of images and video in the uncoded transmission, to achieve the optimal transmission performance. Next, we investigate the resource allocation problem for uncoded transmission, including subchannel, bandwidth and power allocation. By properly allocating these resources, uncoded transmission can achieve higher efficiency and more robust performance. Subsequently, we consider the image and video delivery in MIMO broadcasting networks with diverse channel quality and varying numbers of antennas across receivers. Finally, we investigate the cases where uncoded transmission can be used in conjunction with digital transmission for a balanced efficiency and adaptation capability.This book is the very first monograph in the general area of uncoded multimedia transmission written in a self-contained format. It addresses both the fundamentals and the applications of uncoded transmission. It gives a systematic introduction to the fundamental theory and concepts in this field, and at the same time, also presents specific applications that reveal the great potential and impacts for the technologies generated from the research in this field. By concentrating several important studies and developments currently taking place in the field of uncoded transmission in a single source, this book can reduce the time and cost required to learn and improve skills and knowledge in the field.The authors have been actively working in this field for years, and this book is the final essence of their years of long research in this field. The book may be used as a collection of research notes for researchers in this field, a reference book for practitioners or engineers, as well as a textbook for a graduate advanced seminar in this field or any related fields. The references collected in this book may be used as further reading lists or references for the readers.

This book focuses on seven commonly used image analysis techniques. It covers aspects from basic principles and practical methods, to new advancement of each selected technique to help readers solve image‐processing related problems in real-life situations. The selected techniques include image segmentation, segmentation evaluation and comparison, saliency object detection, motion analysis, mathematical morphology methods, face recognition and expression classification. The author offers readers a three‐step strategy toward problem‐solving: first, essential principles; then, a detailed explanation; and finally, a discussion on practical and working techniques for specific tasks. He also encourages readers to make full use of available materials from the latest developments and trends. This is an excellent book for those who do not have a complete foundation in image technology but need to use image analysis techniques to perform specific tasks in particular applications.

A Selection of Image Processing Techniques: From Fundamentals to Research Front focuses on seven commonly used image-processing techniques. These are de-noising, de-blurring, repairing, de-fogging, reconstruction from projection, watermarking, and super-resolution. This book is suitable for readers who do not have a complete foundation in the principles of image technology but need to use image techniques to solve specific tasks in particular applications. Hence, elementary knowledge for further study is provided, allowing the reader to discover suitable techniques for solving practical problems and to learn the latest developments in a specific domain. This book offers readers a three-step strategy toward problem solving: first, essential principles, then, a detailed explanation, and finally, a discussion of practical and working techniques for specific tasks. Throughout, the author highlights materials pertaining to the newest developments and trends of the technologies.

Fundamentals of Image, Audio, and Video Processing Using MATLAB® introduces the concepts and principles of media processing and its applications in pattern recognition by adopting a hands-on approach using program implementations. The book covers the tools and techniques for reading, modifying, and writing image, audio, and video files using the data analysis and visualization tool MATLAB®. Key Features: Covers fundamental concepts of image, audio, and video processing Demonstrates the use of MATLAB® on solving problems on media processing Discusses important features of Image Processing Toolbox, Audio System Toolbox, and Computer Vision Toolbox MATLAB® codes are provided as answers to specific problems Illustrates the use of Simulink for audio and video processing Handles processing techniques in both the Spatio-Temporal domain and Frequency domain This is a perfect companion for graduate and post-graduate students studying courses on image processing, speech and language processing, signal processing, video object detection and tracking, and related multimedia technologies, with a focus on practical implementations using programming constructs and skill developments. It will also appeal to researchers in the field of pattern recognition, computer vision and content-based retrieval, and for students of MATLAB® courses dealing with media processing, statistical analysis, and data visualization.Dr. Ranjan Parekh, PhD (Engineering), is Professor at the School of Education Technology, Jadavpur University, Calcutta, India, and is involved with teaching subjects related to Graphics and Multimedia at the post-graduate level. His research interest includes multimedia information processing, pattern recognition, and computer vision.

Mycotoxins are secondary metabolites produced by fungi in a wide range of foods (cereals, peanut, tree nuts, dried fruits, coffee, cocoa, grapes, spices…) both in the field and after harvest, particularly during storage. They can also be found in processed foods of plant origin, or by transfer, in food products of animal (milk, eggs, meat and offal). Mycotoxins are of major concern since they can cause acute or chronic intoxications in both humans and animals which are sometimes fatal. Many countries, particularly in Europe, have set maximum acceptable levels for mycotoxins in food and feed.The book reviews the latest literature and innovations on important aspects of mycotoxins, e.g. mycotoxin producing fungi and the related ecosystems, mycotoxin occurrence, toxicity, analysis and management. Quantitative estimation of impacts of climate change on mycotoxin occurrence have been made recently, using predictive modelling. There is also a growing interest in studying the occurrence and toxicity of multiple mycotoxins in food and feed, including emerging or modified forms of mycotoxins. Innovative tools have also developed to detect and quantify toxinogenic fungi and their toxins. In order to reduce the use of chemicals that are harmful to the environment and health of consumers, alternative methods of prevention and decontamination of mycotoxins were tested in pre- and post-harvest, using microorganisms, natural substances or radiation treatments.

Visual Perception and Control of Underwater Robots covers theories and applications from aquatic visual perception and underwater robotics. Within the framework of visual perception for underwater operations, image restoration, binocular measurement, and object detection are addressed. More specifically, the book includes adversarial critic learning for visual restoration, NSGA-II-based calibration for binocular measurement, prior knowledge refinement for object detection, analysis of temporal detection performance, as well as the effect of the aquatic data domain on object detection. With the aid of visual perception technologies, two up-to-date underwater robot systems are demonstrated. The first system focuses on underwater robotic operation for the task of object collection in the sea. The second is an untethered biomimetic robotic fish with a camera stabilizer, its control methods based on visual tracking. The authors provide a self-contained and comprehensive guide to understand underwater visual perception and control. Bridging the gap between theory and practice in underwater vision, the book features implementable algorithms, numerical examples, and tests, where codes are publicly available. Additionally, the mainstream technologies covered in the book include deep learning, adversarial learning, evolutionary computation, robust control, and underwater bionics. Researchers, senior undergraduate and graduate students, and engineers dealing with underwater visual perception and control will benefit from this work.

This book covers the technology of digital image processing in various fields with big data and their applications. Readers will understand various technologies and strategies used in digital image processing as well as handling big data, using machine-learning techniques. This book will help to improve the skills of students and researchers in such fields as engineering, agriculture, and medical imaging. There is a need to be able to understand and analyse the latest developments of digital image technology. As such, this book will cover:· Applications such as biomedical science and biometric image processing, content-based image retrieval, remote sensing, pattern recognition, shape and texture analysis· New concepts in color interpolation to produce the full color from the sub-pattern bare pattern color prevalent in today's digital cameras and other imaging devices· Image compression standards that are needed to serve diverse applications· Applications of remote sensing, medical science, traffic management, education, innovation, and analysis in agricultural design and image processing· Both soft and hard computing approaches at great length in relation to major image processing tasks· The direction and development of current and future research in many areas of image processing· A comprehensive bibliography for additional research (integrated within the framework of the book)This book focuses not only on theoretical and practical knowledge in the field but also on the traditional and latest tools and techniques adopted in image processing and data science. It also provides an indispensable guide to a wide range of basic and advanced techniques in the fields of image processing and data science.

In today's world, deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However, most people are missing the educational tools to make use of this resource. Deep Learning for Remote Sensing Images with Open Source Software is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing, including landcover mapping, forestry, urban studies, disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data. Specific Features of this Book: The first book that explains how to apply deep learning techniques to public, free available data (Spot-7 and Sentinel-2 images, OpenStreetMap vector data), using open source software (QGIS, Orfeo ToolBox, TensorFlow) Presents approaches suited for real world images and data targeting large scale processing and GIS applications Introduces state of the art deep learning architecture families that can be applied to remote sensing world, mainly for landcover mapping, but also for generic approaches (e.g. image restoration) Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills. Includes deep learning techniques through many step by step remote sensing data processing exercises.

For optimal computer vision outcomes, attention to image pre-processing is required so that one can improve image features by eliminating unwanted falsification. This book emphasizes various image pre-processing methods which are necessary for early extraction of features from the image. Effective use of image pre-processing can offer advantages and resolve complications that finally results in improved detection of local and global features. Different approaches for image enrichments and improvements are conferred in this book that will affect the feature analysis depending on how the procedures are employed.Key Features Describes the methods used to prepare images for further analysis which includes noise removal, enhancement, segmentation, local, and global feature description Includes image data pre-processing for neural networks and deep learning Covers geometric, pixel brightness, filtering, mathematical morphology transformation, and segmentation pre-processing techniques Illustrates a combination of basic and advanced pre-processing techniques essential to computer vision pipeline Details complications to resolve using image pre-processing

This book emphasizes various image shape feature extraction methods which are necessary for image shape recognition and classification. Focussing on a shape feature extraction technique used in content-based image retrieval (CBIR), it explains different applications of image shape features in the field of content-based image retrieval. Showcasing useful applications and illustrating examples in many interdisciplinary fields, the present book is aimed at researchers and graduate students in electrical engineering, data science, computer science, medicine, and machine learning including medical physics and information technology.

Not only does almost everyone in the civilized world use a personal computer, smartphone, and/or tablet on a daily basis to communicate with others and access information, but virtually every other modern appliance, vehicle, or other device has one or more computers embedded inside it. One cannot purchase a current-model automobile, for example, without several computers on board to do everything from monitoring exhaust emissions, to operating the anti-lock brakes, to telling the transmission when to shift, and so on. Appliances such as clothes washers and dryers, microwave ovens, refrigerators, etc. are almost all digitally controlled. Gaming consoles like Xbox, PlayStation, and Wii are powerful computer systems with enhanced capabilities for user interaction. Computers are everywhere, even when we don't see them as such, and it is more important than ever for students who will soon enter the workforce to understand how they work.This book is completely updated and revised for a one-semester upper level undergraduate course in Computer Architecture, and suitable for use in an undergraduate CS, EE, or CE curriculum at the junior or senior level. Students should have had a course(s) covering introductory topics in digital logic and computer organization. While this is not a text for a programming course, the reader should be familiar with computer programming concepts in at least one language such as C, C++, or Java. Previous courses in operating systems, assembly language, and/or systems programming would be helpful, but are not essential.

Written for senior-level and first year graduate students in biomedical signal and image processing, this book describes fundamental signal and image processing techniques that are used to process biomedical information. The book also discusses application of these techniques in the processing of some of the main biomedical signals and images, such as EEG, ECG, MRI, and CT. New features of this edition include the technical updating of each chapter along with the addition of many more examples, the majority of which are MATLAB based.