MPEG-4 is the multimedia standard for combining interactivity, natural and synthetic digital video, audio and computer-graphics. Typical applications are: internet, video conferencing, mobile videophones, multimedia cooperative work, teleteaching and games. With MPEG-4 the next step from block-based video (ISO/IEC MPEG-1, MPEG-2, CCITT H.261, ITU-T H.263) to arbitrarily-shaped visual objects is taken. This significant step demands a new methodology for system analysis and design to meet the considerably higher flexibility of MPEG-4. Motion estimation is a central part of MPEG-1/2/4 and H.261/H.263 video compression standards and has attracted much attention in research and industry, for the following reasons: it is computationally the most demanding algorithm of a video encoder (about 60-80% of the total computation time), it has a high impact on the visual quality of a video encoder, and it is not standardized, thus being open to competition. Algorithms, Complexity Analysis, and VLSI Architectures for MPEG-4 Motion Estimation covers in detail every single step in the design of a MPEG-1/2/4 or H.261/H.263 compliant video encoder: Fast motion estimation algorithms Complexity analysis tools Detailed complexity analysis of a software implementation of MPEG-4 video Complexity and visual quality analysis of fast motion estimation algorithms within MPEG-4 Design space on motion estimation VLSI architectures Detailed VLSI design examples of (1) a high throughput and (2) a low-power MPEG-4 motion estimator. Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4 Motion Estimation is an important introduction to numerous algorithmic, architectural and system design aspects of the multimedia standard MPEG-4. As such, all researchers, students and practitioners working in image processing, video coding or system and VLSI design will find this book of interest.

Adobe Photoshop Elements Advanced Editing Techniques and Tricks: The Essential Guide to Going Beyond Guided Edits is a must for those who want to go beyond automated features and Guided Edits and delve into the many advanced techniques that are possible using Adobe Photoshop Elements. LEARN HOW TO Perfect editing portrait images by performing techniques such as skin tone correction, frequency separations, skin smoothing, and enhancing facial features Properly edit and enhance a subject's eyes, lips, eyebrows, and facial lighting Apply advanced photo compositing techniques, utilizing rules for controlling perspective Use color grading techniques similar to those used by professional motion picture film editors Delve into advanced tools not included in Photoshop Elements such as curves, color range, selective color, working with color LUTs, and more With detailed step-by-step instructions, this book is targeted to intermediate and advanced users who want to take their photography to the next level. Additional tips using Photoshop Elements can be found on Ted's YouTube channel at www.YouTube/tedpadova.

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.

Introduces the reader to the technical aspects of real-time visual effects. Built upon a career of over twenty years in the feature film visual effects and the real-time video game industries and tested on graduate and undergraduate students. Explores all real-time visual effects in four categories: in-camera effects, in-material effects, simulations and particles.

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 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 is the third edition of the first ever book to explore the exciting field of augmented reality art and its enabling technologies. The new edition has been thoroughly revised and updated, with 9 new chapters included. As well as investigating augmented reality as a novel artistic medium, the book covers cultural, social, spatial and cognitive facets of augmented reality art. It has been written by a virtual team of 33 researchers and artists from 11 countries who are pioneering in the new form of art, and contains numerous colour illustrations showing both classic and recent augmented reality artworks. Intended as a starting point for exploring this new fascinating area of research and creative practice, it will be essential reading not only for artists, researchers and technology developers, but also for students (graduates and undergraduates) and all those interested in emerging augmented reality technology and its current and future applications in art.

The goal of this volume is to summarize the state-of-the-art in the utilization of computer vision techniques in the diagnosis of skin cancer. Malignant melanoma is one of the most rapidly increasing cancers in the world. Early diagnosis is particularly important since melanoma can be cured with a simple excision if detected early. In recent years, dermoscopy has proved valuable in visualizing the morphological structures in pigmented lesions. However, it has also been shown that dermoscopy is difficult to learn and subjective. Newer technologies such as infrared imaging, multispectral imaging, and confocal microscopy, have recently come to the forefront in providing greater diagnostic accuracy. These imaging technologies presented in this book can serve as an adjunct to physicians and provide automated skin cancer screening. Although computerized techniques cannot as yet provide a definitive diagnosis, they can be used to improve biopsy decision-making as well as early melanoma detection, especially for patients with multiple atypical nevi.

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.

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.

Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout.

The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year.

This edited volume contains a selection of articles covering some of the talks and tutorials held during the last editions of the school. The chapters provide an in-depth overview of challenging areas with key references to the existing literature.

Describes the operating principles of analog MOS integrated circuits and how to design and use such circuits. The initial section explores general properties of analog MOS integrated circuits and the math and physics background required. The remainder of the book is devoted to the design of circuits. Includes such devices as switched-capacitor filters, analog-to-digital and digital-to-analog converters, amplifiers, modulators, oscillators, and others. Tables and numerical design examples clarify the step-by-step processes involved.

This book analyzes the multimodal verbal and nonverbal behavior of humans in both an artificial game, based on the well-known Mafia and Resistance games, as well as selected other settings. This book develops statistical results linking different types of facial expressions (e.g. smile, pursed lips, raised eyebrows), vocal features (e.g., pitch, loudness) and linguistic features (e.g., dominant language, turn length) with both unary behaviors (e.g. is person X lying?) to binary behaviors (Is person X dominant compared to person Y? Does X trust Y? Does X like Y?). In addition, this book describes machine learning and computer vision-based algorithms that can be used to predict deception, as well as the visual focus of attention of people during discussions that can be linked to many binary behaviors. It is written by a multidisciplinary team of both social scientists and computer scientists. Meetings are at the very heart of human activity. Whether you are involved in a business meeting or in a diplomatic negotiation, such an event has multiple actors, some cooperative and some adversarial. Some actors may be deceptive, others may have complex relationships with others in the group. This book consists of a set of 11 chapters that describe the factors that link human behavior in group settings and attitudes to facial and voice characteristics. Researchers working in social sciences (communication, psychology, cognitive science) with an interest in studying the link between human interpersonal behavior and facial/speech/linguistic characteristics will be interested in this book. Computer scientists, who are interested in developing machine learning and deep learning based models of human behavior in group settings will also be interested in purchasing this book.

This book gathers selected, extended and revised contributions to the 16th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering, and the 4th Conference on Imaging and Visualization (CMBBE 2019), held on August 14-16, 2019, in New York City, USA. It reports on cutting-edge models and algorithms for studying various tissues and organs in normal and pathological conditions; innovative imaging and visualization techniques; and the latest diagnostic tools. Further topics addressed include: numerical methods, machine learning approaches, FEM models, and high-resolution imaging and real-time visualization methods applied for biomedical purposes. Given the scope of its coverage, the book provides graduate students and researchers with a timely and insightful snapshot of the latest research and current challenges in biomedical engineering, computational biomechanics and biological imaging, as well as a source of inspiration for future research and cross-disciplinary collaborations.

Fourier Vision provides a new treatment of figure-ground segmentation in scenes comprising transparent, translucent, or opaque objects. Exploiting the relative motion between figure and ground, this technique deals explicitly with the separation of additive signals and makes no assumptions about the spatial or spectral content of the images, with segmentation being carried out phasor by phasor in the Fourier domain. It works with several camera configurations, such as camera motion and short-baseline binocular stereo, and performs best on images with small velocities/displacements, typically one to ten pixels per frame. The book also addresses the use of Fourier techniques to estimate stereo disparity and optical flow. Numerous examples are provided throughout. Fourier Vision will be of value to researchers in image processing & computer vision and, especially, to those who have to deal with superimposed transparent or translucent objects. Researchers in application areas such as medical imaging and acoustic signal processing will also find this of interest.

This book constitutes the refereed proceedings of two International Workshops held as parallel events of the 16th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2020, in Neos Marmaras, Greece, in June 2020: the 9th Mining Humanistic Data Workshop, MHDW 2020, and the 5th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2020.* The 6 full papers and 3 short papers presented at MHDW 2020 were carefully reviewed and selected from 16 submissions; out of the 23 papers submitted to 5G-PINE 2020, 11 were accepted as full papers and 1 as a short paper. The MHDW papers focus on topics such as recommendation systems, sentiment analysis, pattern recognition, data mining, and time series. The papers presented at 5G-PINE focus on the latest AI applications in the telecommunication industry and deal with topics such as the Internet of Things, intelligence fusion in 5G networks, and 5G media. *The workshops were held virtually due to the COVID-19 pandemic.

Cardiovascular and Coronary Artery Imaging, Volume One covers state-of-the-art approaches for automated non-invasive systems in early cardiovascular disease diagnosis. The book includes several prominent imaging modalities, such as MRI, CT and PET technologies. A special emphasis is placed on automated imaging analysis techniques, which are important to biomedical imaging analysis of the cardiovascular system. This is a comprehensive, multi-contributed reference work that details the latest developments in spatial, temporal and functional cardiac imaging.

"Biometrics andKansei Engineering "is the first book to bring together the principles and applications of each discipline. The future of biometrics is in need of new technologies that can depend on people's emotions and the prediction of their intention to take an action. Behavioral biometrics studies the way people walk, talk, and express their emotions, and Kansei Engineering focuses on interactions between users, products/services and product psychology. They are becoming quite complementary.

This book also introduces biometric applications in our environment, which further illustrates the close relationship between Biometrics and Kansei Engineering. Examples and case studies are provided throughout this book.

"Biometrics and Kansei Engineering "is designed as a reference book for professionals working in these related fields. Advanced-level students and researchers studying computer science and engineering will find this book useful as a reference or secondary text book as well. "

This book presents revised versions of the best papers selected from the symposium Mathematical Progress in Expressive Image Synthesis (MEIS2013) held in Fukuoka, Japan, in 2013. The topics cover various areas of computer graphics (CG), such as surface deformation/editing, character animation, visual simulation of fluids, texture and sound synthesis and photorealistic rendering. From a mathematical point of view, the book also presents papers addressing discrete differential geometry, Lie theory, computational fluid dynamics, function interpolation and learning theory. This book showcases the latest joint efforts between mathematicians, CG researchers and practitioners exploring important issues in graphics and visual perception.The book provides a valuable resource for all computer graphics researchers seeking open problem areas, especially those now entering the field who have not yet selected a research direction."

Computer vision falls short of human vision in two respects: execution time and intelligent interpretation. This book addresses the question of execution time. It is based on a workshop on specialized processors for real-time image analysis, held as part of the activities of an ESPRIT Basic Research Action, the Working Group on Vision. The aim of the book is to examine the state of the art in vision-oriented computers. Two approaches are distinguished: multiprocessor systems and fine-grain massively parallel computers. The development of fine-grain machines has become more important over the last decade, but one of the main conclusions of the workshop is that this does not imply the replacement of multiprocessor machines. The book is divided into four parts. Part 1 introduces different architectures for vision: associative and pyramid processors as examples of fine-grain machines and a workstation with bus-oriented network topology as an example of a multiprocessor system. Parts 2 and 3 deal with the design and development of dedicated and specialized architectures. Part 4 is mainly devoted to applications, including road segmentation, mobile robot guidance and navigation, reconstruction and identification of 3D objects, and motion estimation.

Semantic Video Object Segmentation for Content-Based Multimedia Applications provides a thorough review of state-of-the-art techniques as well as describing several novel ideas and algorithms for semantic object extraction from image sequences. Semantic object extraction is an essential element in content-based multimedia services, such as the newly developed MPEG4 and MPEG7 standards. An interactive system called SIVOG (Smart Interactive Video Object Generation) is presented, which converts user's semantic input into a form that can be conveniently integrated with low-level video processing. Thus, high-level semantic information and low-level video features are integrated seamlessly into a smart segmentation system. A region and temporal adaptive algorithm was further proposed to improve the efficiency of the SIVOG system so that it is feasible to achieve nearly real-time video object segmentation with robust and accurate performances. Also included is an examination of the shape coding problem and the object segmentation problem simultaneously. Semantic Video Object Segmentation for Content-Based Multimedia Applications will be of great interest to research scientists and graduate-level students working in the area of content-based multimedia representation and applications and its related fields.

This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.

fMRI Neurofeedback provides a perspective on how the field of functional magnetic resonance imaging (fMRI) neurofeedback has evolved, an introduction to state-of-the-art methods used for fMRI neurofeedback, a review of published neuroscientific and clinical applications, and a discussion of relevant ethical considerations. It gives a view of the ongoing research challenges throughout and provides guidance for researchers new to the field on the practical implementation and design of fMRI neurofeedback protocols. This book is designed to be accessible to all scientists and clinicians interested in conducting fMRI neurofeedback research, addressing the variety of different knowledge gaps that readers may have given their varied backgrounds and avoiding field-specific jargon. The book, therefore, will be suitable for engineers, computer scientists, neuroscientists, psychologists, and physicians working in fMRI neurofeedback.