This book covers different, current research directions in the context of variational methods for non-linear geometric data. Each chapter is authored by leading experts in the respective discipline and provides an introduction, an overview and a description of the current state of the art. Non-linear geometric data arises in various applications in science and engineering. Examples of nonlinear data spaces are diverse and include, for instance, nonlinear spaces of matrices, spaces of curves, shapes as well as manifolds of probability measures. Applications can be found in biology, medicine, product engineering, geography and computer vision for instance. Variational methods on the other hand have evolved to being amongst the most powerful tools for applied mathematics. They involve techniques from various branches of mathematics such as statistics, modeling, optimization, numerical mathematics and analysis. The vast majority of research on variational methods, however, is focused on data in linear spaces. Variational methods for non-linear data is currently an emerging research topic. As a result, and since such methods involve various branches of mathematics, there is a plethora of different, recent approaches dealing with different aspects of variational methods for nonlinear geometric data. Research results are rather scattered and appear in journals of different mathematical communities. The main purpose of the book is to account for that by providing, for the first time, a comprehensive collection of different research directions and existing approaches in this context. It is organized in a way that leading researchers from the different fields provide an introductory overview of recent research directions in their respective discipline. As such, the book is a unique reference work for both newcomers in the field of variational methods for non-linear geometric data, as well as for established experts that aim at to exploit new research directions or collaborations. Chapter 9 of this book is available open access under a CC BY 4.0 license at link.springer.com.

This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition. Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods. This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.

This book comprehensively reviews the various automated and semi-automated signal and image processing techniques, as well as deep-learning-based image analysis techniques, used in healthcare diagnostics. It highlights a range of data pre-processing methods used in signal processing for effective data mining in remote healthcare, and discusses pre-processing using filter techniques, noise removal, and contrast-enhanced methods for improving image quality. The book discusses the status quo of artificial intelligence in medical applications, as well as its future. Further, it offers a glimpse of feature extraction methods for reducing dimensionality and extracting discriminatory information hidden in biomedical signals. Given its scope, the book is intended for academics, researchers and practitioners interested in the latest real-world technological innovations.

This book deals with two fundamental issues in the semiotics of the image. The first is the relationship between image and observer: how does one look at an image? To answer this question, this book sets out to transpose the theory of enunciation formulated in linguistics over to the visual field. It also aims to clarify the gains made in contemporary visual semiotics relative to the semiology of Roland Barthes and Emile Benveniste. The second issue addressed is the relation between the forces, forms and materiality of the images. How do different physical mediums (pictorial, photographic and digital) influence visual forms? How does materiality affect the generativity of forms? On the forces within the images, the book addresses the philosophical thought of Gilles Deleuze and Rene Thom as well as the experiment of Aby Warburg's Atlas Mnemosyne. The theories discussed in the book are tested on a variety of corpora for analysis, including both paintings and photographs, taken from traditional as well as contemporary sources in a variety of social sectors (arts and sciences). Finally, semiotic methodology is contrasted with the computational analysis of large collections of images (Big Data), such as the "Media Visualization" analyses proposed by Lev Manovich and Cultural Analytics in the field of Computer Science to evaluate the impact of automatic analysis of visual forms on Digital Art History and more generally on the image sciences.

Addresses a wide selection of multimedia applications, programmable and custom architectures for the implementations of multimedia systems, and arithmetic architectures and design methodologies. The book covers recent applications of digital signal processing algorithms in multimedia, presents high-speed and low-priority binary and finite field arithmetic architectures, details VHDL-based implementation approaches, and more.

This book is a complete introduction to vector analysis, especially within the context of computer graphics. The author shows why vectors are useful and how it is possible to develop analytical skills in manipulating vector algebra. Even though vector analysis is a relatively recent development in the history of mathematics, it has become a powerful and central tool in describing and solving a wide range of geometric problems. The book is divided into eleven chapters covering the mathematical foundations of vector algebra and its application to, among others, lines, planes, intersections, rotating vectors, and vector differentiation.

PREVIOUS EDITIONThis textbook introduces the "Fundamentals of Multimedia", addressing real issues commonly faced in the workplace. The essential concepts are explained in a practical way to enable students to apply their existing skills to address problems in multimedia. Fully revised and updated, this new edition now includes coverage of such topics as 3D TV, social networks, high-efficiency video compression and conferencing, wireless and mobile networks, and their attendant technologies. Features: presents an overview of the key concepts in multimedia, including color science; reviews lossless and lossy compression methods for image, video and audio data; examines the demands placed by multimedia communications on wired and wireless networks; discusses the impact of social media and cloud computing on information sharing and on multimedia content search and retrieval; includes study exercises at the end of each chapter; provides supplementary resources for both students and instructors at an associated website.

Signal processing is a broad and timeless area. The term "signal" includes audio, video, speech, image, communication, geophysical, sonar, radar, medical, and more. Signal processing applies to the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.
Handbook of Formulas and Tables for Signal Processing a must-have reference for all engineering professionals involved in signal and image processing. Collecting the most useful formulas and tables - such as integral tables, formulas of algebra, formulas of trigonometry - the text includes:
o Material for the deterministic and statistical signal processing areas
o Examples explaining the use of the given formula
o Numerous definitions
o Many figures that have been added to special chapters
Handbook of Formulas and Tables for Signal Processing brings together - in one textbook - all the equations necessary for signal and image processing for professionals transforming anything from a physical to a manipulated form, creating a new standard for any person starting a future in the broad, extensive area of research.

Minimizing theoretical background and mathematical formalism, Image Analysis provides basic principles of image acquisition, enhancement, measurements, and interpretation in a very simple form, using an approach toward applications and properties of available tools. The singular study lists different tasks to do and offers complete solutions to these tasks, based on the author's experience with the procedures described.

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 two volume set (CCIS 1451 and 1452) constitutes the refereed proceedings of the 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021 held in Taiyuan, China, in September 2021.The 81 papers presented in these two volumes were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on big data management and applications; social media and recommendation systems; infrastructure for data science; basic theory and techniques for data science; machine learning for data science; multimedia data management and analysis; social media and recommendation systems; data security and privacy; applications of data science; education research, methods and materials for data science and engineering; research demo.

High Dynamic Range Video: Concepts, Technologies and Applications gives an introduction to a full range of topics within the end-to-end HDR video pipeline, covering the issues around capturing HDR and stereo HDR video, such as ghosting and use of legacy LDR systems, how HDR video can be manipulated, including real-time mixing, the very latest designs for HDR displays, HDR video on mobile devices, and the applications of HDR video. With this book, the reader will gain an overview of the current state-of-the art of HDR video, learn the potential of HDR video to provide a step change to a wide range of imaging applications, and attain the knowledge needed to introduce HDR video in their own applications.

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.

Wavelet analysis is among the newest additions to the arsenals of mathematicians, scientists, and engineers, and offers common solutions to diverse problems. However, students and professionals in some areas of engineering and science, intimidated by the mathematical background necessary to explore this subject, have been unable to use this powerful tool.

The first book on the topic for readers with minimal mathematical backgrounds, Wavelet Analysis with Applications to Image Processing provides a thorough introduction to wavelets with applications in image processing. Unlike most other works on this subject, which are often collections of papers or research advances, this book offers students and researchers without an extensive math background a step-by-step introduction to the power of wavelet transforms and applications to image processing.
The first four chapters introduce the basic topics of analysis that are vital to understanding the mathematics of wavelet transforms. Subsequent chapters build on the information presented earlier to cover the major themes of wavelet analysis and its applications to image processing. This is an ideal introduction to the subject for students, and a valuable reference guide for professionals working in image processing.

Presenting statistical and stochastic methods for the analysis and design of technological systems in engineering and applied areas, this work documents developments in statistical modelling, identification, estimation and signal processing. The book covers such topics as subspace methods, stochastic realization, state space modelling, and identification and parameter estimation.

Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques

Modern science requires computer graphics models to provide realistic visual renderings. Learning the appropriate programming tools for 2D and 3D modeling doesn't have to be so difficult. This book reviews the best programming tools to achieve this and explains how to apply them to mobile platforms like Android. Multi-Platform Graphics Programming with Kivy provides a straightforward introductory approach for designing 2D, 3D, and stereoscopic applications, using analytical equations from vector algebra. Throughout the book you'll look closely at this approach and develop scenes in Kivy, taking advantage of powerful mathematical functions for arrays by NumPy for Python. Unbuntu is used to develop the programs, which allows you to easily convert to Android platform. Each chapter contains step-by-step descriptions on each subject and provides complete program listings. What You'll Learn Work with Kivy, a modern, powerful multi-platform graphics system Convert and run programs on Android devices Program, fill faces, and rotate 2D and 3D polygons Apply the concepts of 2D and 3D applications Develop stereoscopic scenes Review a straightforward introduction to 2D, 3D, and stereoscopic graphics applications Use simple analytical equations from vector algebra Who This Book Is For The primary audience is students and researchers in graphics programming with experience in analytical equations.

Multi-Modal User Interactions in Controlled Environments investigates the capture and analysis of user's multimodal behavior (mainly eye gaze, eye fixation, eye blink and body movements) within a real controlled environment (controlled-supermarket, personal environment) in order to adapt the response of the computer/environment to the user. Such data is captured using non-intrusive sensors (for example, cameras in the stands of a supermarket) installed in the environment. This multi-modal video based behavioral data will be analyzed to infer user intentions while assisting users in their day-to-day tasks by adapting the system's response to their requirements seamlessly. This book also focuses on the presentation of information to the user. Multi-Modal User Interactions in Controlled Environments is designed for professionals in industry, including professionals in the domains of security and interactive web television. This book is also suitable for graduate-level students in computer science and electrical engineering.

This book presents the reader with comprehensive insight into various kinds of mathematical modeling and numerical computation for problems arising in several branches of engineering, such as mechanical engineering, computer science engineering, electrical engineering, electronics and communication engineering, and civil engineering. The book: * Discusses topics related to clean and green energy production and storage * Bridges the gap between core theory and costly industrial experiments * Covers advanced biomechanics and nanodrug delivery topics * Explores diversified applications of mathematical techniques to solve practical engineering problems The text in this book emphasizes mathematical treatment of soft computing, image and signal processing, fluid flows in various geometries, biomechanics, biological modeling, a mathematical description of the solar cell, analytical and numerical treatment of problems in fracture mechanics, and antenna design modeling. It also discusses the numerical computations of biomechanics problems and problems arising in cryptography. The text further covers optimization techniques that are useful for real-world problems. This material is primarily written for graduate students and academic researchers in a number of engineering fields, including electrical, electronics and communication, industrial, manufacturing, mechanical, computer science, and mathematics.

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.

GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems. Finally, advanced material includes CUDA code in MATLAB and optimizing existing GPU applications. Throughout the book, examples and source codes illustrate every concept so that readers can immediately apply them to their own development.

This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

This book contains the latest computational intelligence methodologies and applications. This book is a collection of selected papers presented at International Conference on Sustainable Computing and Intelligent Systems (SCIS 2021), held in Jaipur, India, during February 5-6, 2021. 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 artificial intelligence and intelligent systems, intelligent business systems, machine intelligence, computer vision, Web intelligence, big data analytics, swarm intelligence, and related topics.

With the vast development of Internet capacity and speed, as well as wide adop- tion of media technologies in people's daily life, a large amount of videos have been surging, and need to be efficiently processed or organized based on interest. The human visual perception system could, without difficulty, interpret and r- ognize thousands of events in videos, despite high level of video object clutters, different types of scene context, variability of motion scales, appearance changes, occlusions and object interactions. For a computer vision system, it has been be very challenging to achieve automatic video event understanding for decades. Broadly speaking, those challenges include robust detection of events under - tion clutters, event interpretation under complex scenes, multi-level semantic event inference, putting events in context and multiple cameras, event inference from object interactions, etc. In recent years, steady progress has been made towards better models for video event categorisation and recognition, e. g. , from modelling events with bag of spatial temporal features to discovering event context, from detecting events using a single camera to inferring events through a distributed camera network, and from low-level event feature extraction and description to high-level semantic event classification and recognition. Nowadays, text based video retrieval is widely used by commercial search engines. However, it is still very difficult to retrieve or categorise a specific video segment based on their content in a real multimedia system or in surveillance applications.

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.