The rise in living standards increases the expectation of people in almost every field. At the forefront is health. Over the past few centuries, there have been major developments in healthcare. Medical device technology and developments in artificial intelligence (AI) are among the most important ones. The improving technology and our ability to harness the technology effectively by means such as AI have led to unprecedented advances, resulting in early diagnosis of diseases. AI algorithms enable the fast and early evaluation of images from medical devices to maximize the benefits. While developments in the field of AI were quickly adapted to the field of health, in some cases this contributed to the formation of innovative artificial intelligence algorithms. Today, the most effective artificial intelligence method is accepted as deep learning. Convolutional neural network (CNN) architectures are deep learning algorithms used for image processing. This book contains applications of CNN methods. The content is quite extensive, including the application of different CNN methods to various medical image processing problems. Readers will be able to analyze the effects of CNN methods presented in the book in medical applications.

This book contains extended, in-depth presentations of the plenary talks from the 16th French-German-Polish Conference on Optimization, held in Krakow, Poland in 2013. Each chapter in this book exhibits a comprehensive look at new theoretical and/or application-oriented results in mathematical modeling, optimization, and optimal control. Students and researchers involved in image processing, partial differential inclusions, shape optimization, or optimal control theory and its applications to medical and rehabilitation technology, will find this book valuable. The first chapter by Martin Burger provides an overview of recent developments related to Bregman distances, which is an important tool in inverse problems and image processing. The chapter by Piotr Kalita studies the operator version of a first order in time partial differential inclusion and its time discretization. In the chapter by Gunter Leugering, Jan Sokolowski and Antoni Zochowski, nonsmooth shape optimization problems for variational inequalities are considered. The next chapter, by Katja Mombaur is devoted to applications of optimal control and inverse optimal control in the field of medical and rehabilitation technology, in particular in human movement analysis, therapy and improvement by means of medical devices. The final chapter, by Nikolai Osmolovskii and Helmut Maurer provides a survey on no-gap second order optimality conditions in the calculus of variations and optimal control, and a discussion of their further development.

Digital Image Processing with C++ 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 in a broad way the field of digital image processing and proposes 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, teachers and researchers wishing 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.

Lying at the intersection of education, art, and cultural heritage, visualization is a powerful tool for representing and interpreting complex information.

This unique text/reference reviews the evolution of the field of visualization, providing innovative examples of applied knowledge visualization from disciplines as varied as law, business management, the arts and humanities. With coverage of theoretical and practical aspects of visualization from ancient Sumerian tablets through to twenty-first century legal contracts, this work underscores the important role that the process of visualization plays in extracting, organizing, and crystallizing the concepts found in complex data.

Topics and features: contains contributions from an international selection of preeminent authorities; presents a thorough introduction to the discipline of knowledge visualization, its current state of affairs and possible future developments; examines how tables have been used for information visualization in historical textual documents; discusses the application of visualization techniques for knowledge transfer in business relationships, and for the linguistic exploration and analysis of sensory descriptions; investigates the use of visualization to understand orchestral music scores, the optical theory behind Renaissance art, and to assist in the reconstruction of an historic church; describes immersive 360 degree stereographic visualization, knowledge-embedded embodied interaction, and a novel methodology for the analysis of architectural forms.

This interdisciplinary collection of the state of the art in knowledge visualization will be of considerable interest to researchers from a broad spectrum of backgrounds in both industry and academia.

This book offers a systematic and comprehensive introduction to the visual simultaneous localization and mapping (vSLAM) technology, which is a fundamental and essential component for many applications in robotics, wearable devices, and autonomous driving vehicles. The book starts from very basic mathematic background knowledge such as 3D rigid body geometry, the pinhole camera projection model, and nonlinear optimization techniques, before introducing readers to traditional computer vision topics like feature matching, optical flow, and bundle adjustment. The book employs a light writing style, instead of the rigorous yet dry approach that is common in academic literature. In addition, it includes a wealth of executable source code with increasing difficulty to help readers understand and use the practical techniques. The book can be used as a textbook for senior undergraduate or graduate students, or as reference material for researchers and engineers in related areas.

This book opens with an introduction to the main purpose and tasks of the GIANA challenge, as well as a summary and an analysis of the results and performance obtained by the 20 participating teams. The early and accurate diagnosis of gastrointestinal diseases is critical for increasing the chances of patient survival, and efficient screening is vital for locating precursor lesions. Video colonoscopy and wireless capsule endoscopy (WCE) are the gold-standard tools for colon and intestinal tract screening, respectively. Yet these tools still present some drawbacks, such as lesion miss rate, lack of in vivo diagnosis capabilities, and perforation risk. To mitigate these, computer-aided detection/diagnosis systems can play a key role in assisting clinicians in the different stages of the exploration. This book presents the latest, state-of-the-art approaches in this field, and also tackles the clinical considerations required to efficiently deploy these systems in the exploration room. The coverage draws upon results from the Gastrointestinal Image Analysis (GIANA) Challenge, part of the EndoVis satellite events of the conferences MICCAI 2017 and 2018. Each method proposed to address the different subtasks of the challenges is detailed in a separate chapter, offering a deep insight into this topic of interest for public health. This book appeals to researchers, practitioners, and lecturers spanning both the computer vision and gastroenterology communities.

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 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.

This book gives a comprehensive view of the developed procrustes models, including the isotropic, the generalized and the anisotropic variants. These represent original tools to perform, among others, the bundle block adjustment and the global registration of multiple 3D LiDAR point clouds. Moreover, the book also reports the recently derived total least squares solution of the anisotropic Procrustes model, together with its practical application in solving the exterior orientation of one image. The book is aimed at all those interested in discovering valuable innovative algorithms for solving various photogrammetric computer vision problems. In this context, where functional models are non-linear, Procrustean methods prove to be powerful since they do not require any linearization nor approximated values of the unknown parameters, furnishing at the same time results comparable in terms of accuracy with those given by the state-of-the-art methods.

The generation, storage and processing of digital images plays a fundamental role in the information technology revolution. Digital imaging processing technology has developed markedly over the last ten years and more and more information is being conveyed through the display and analysis of digital images. The way in which image data is stored and processed is fundamental to all aspects of IT. Examples include remote sensing using the new generation of digital satellites which carry a range of different sensors that, when coupled with suitable image processing technology, can provide a wealth of information to geologists, geographers and atmospheric physicists used in everything from the exploration of oil and other natural resources to environmental monitoring and agricultural development in the Third World. Other examples include the use of image processing in medical imaging for use in diagnosis using conventional X-ray Computed Tomography to research into the behaviour of the human brain using real time Magnetic Resonance Imaging. This book consists of twenty-one papers which collectively cover a broad range of image processing problems and the way on which solutions to these problems are used in different area of sciences and technology. The papers present details of the way in which computers of varying processing power can be programmed to store image efficiently, resolve features and patterns in an image that are either time consuming or impossible for human interpreters and develop machines that can `see', like humans. The book covers a wide range of applications which include the use of lasers for studying the dynamic behaviour of mechanical components, overviews of image processing in remote sensing and medical imaging and the application of a new form of geometry (fractal geometry) for rcognizing patterns which is not possible with conventional data processing. The book will be of value to any engineer, scientists and technologist who wants to acquire information on current research issues in image processing by reading a set of papers prepared by some of the world's leading specialists.

Group and Crowd Behavior for Computer Vision provides a multidisciplinary perspective on how to solve the problem of group and crowd analysis and modeling, combining insights from the social sciences with technological ideas in computer vision and pattern recognition. The book answers many unresolved issues in group and crowd behavior, with Part One providing an introduction to the problems of analyzing groups and crowds that stresses that they should not be considered as completely diverse entities, but as an aggregation of people. Part Two focuses on features and representations with the aim of recognizing the presence of groups and crowds in image and video data. It discusses low level processing methods to individuate when and where a group or crowd is placed in the scene, spanning from the use of people detectors toward more ad-hoc strategies to individuate group and crowd formations. Part Three discusses methods for analyzing the behavior of groups and the crowd once they have been detected, showing how to extract semantic information, predicting/tracking the movement of a group, the formation or disaggregation of a group/crowd and the identification of different kinds of groups/crowds depending on their behavior. The final section focuses on identifying and promoting datasets for group/crowd analysis and modeling, presenting and discussing metrics for evaluating the pros and cons of the various models and methods. This book gives computer vision researcher techniques for segmentation and grouping, tracking and reasoning for solving group and crowd modeling and analysis, as well as more general problems in computer vision and machine learning.

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.

This book presents novel hybrid encryption algorithms that possess many different characteristics. In particular, "Hybrid Encryption Algorithms over Wireless Communication Channels", examines encrypted image and video data for the purpose of secure wireless communications. A study of two different families of encryption schemes are introduced: namely, permutation-based and diffusion-based schemes. The objective of the book is to help the reader selecting the best suited scheme for the transmission of encrypted images and videos over wireless communications channels, with the aid of encryption and decryption quality metrics. This is achieved by applying number-theory based encryption algorithms, such as chaotic theory with different modes of operations, the Advanced Encryption Standard (AES), and the RC6 in a pre-processing step in order to achieve the required permutation and diffusion. The Rubik's cube is used afterwards in order to maximize the number of permutations. Transmission of images and videos is vital in today's communications systems. Hence, an effective encryption and modulation schemes are a must. The author adopts Orthogonal Frequency Division Multiplexing (OFDM), as the multicarrier transmission choice for wideband communications. For completeness, the author addresses the sensitivity of the encrypted data to the wireless channel impairments, and the effect of channel equalization on the received images and videos quality. Complete simulation experiments with MATLAB (R) codes are included. The book will help the reader obtain the required understanding for selecting the suitable encryption method that best fulfills the application requirements.

This book provides a thorough overview of recent methods using higher level information (object or scene level) for advanced tasks such as image understanding along with their applications to medical images. Advanced methods for fuzzy image processing and understanding are presented, including fuzzy spatial objects, geometry and topology, mathematical morphology, machine learning, verbal descriptions of image content, fusion, spatial relations, and structural representations. For each methodological aspect covered, illustrations from the medical imaging domain are provided. This is an ideal book for graduate students and researchers in the field of medical image processing.

This book gathers outstanding research papers presented in the 2nd International Conference on Artificial Intelligence: Advances and Application (ICAIAA 2021), held in Poornima College of Engineering, Jaipur, India during 27-28 March 2021. This book covers research works carried out by various students such as bachelor, master and doctoral scholars, faculty and industry persons in the area of artificial intelligence, machine learning, deep learning applications in healthcare, agriculture, business, security, etc. It will also cover research in core concepts of computer networks, intelligent system design and deployment, real time systems, WSN, sensors and sensor nodes, SDN, NFV, etc.

This book, written from the perspective of a designer and educator, brings to the attention of media historians, fellow practitioners and students the innovative practices of leading moving image designers. Moving image design, whether viewed as television and movie title sequences, movie visual effects, animating infographics, branding and advertising, or as an art form, is being increasingly recognised as an important dynamic part of contemporary culture. For many practitioners this has been long overdue. Central to these designers' practice is the hybridisation of digital and heritage methods. Macdonald uses interviews with world-leading motion graphic designers, moving image artists and Oscar nominated visual effects supervisors to examine the hybrid moving image, which re-invigorates both heritage practices and the handmade and analogue crafts. Now is the time to ensure that heritage skills do not atrophy, but that their qualities and provenance are understood as potent components with digital practices in new hybrids.

This book presents the latest technological advances and practical tools for discovering, verifying and visualizing social media video content, and managing related rights. The digital media revolution is bringing breaking news to online video platforms, and news organizations often rely on user-generated recordings of new and developing events shared in social media to illustrate the story. However, in video, there is also deception. In today's "fake news" era, access to increasingly sophisticated editing and content management tools and the ease with which fake information spreads in electronic networks, require the entire news and media industries to carefully verify third-party content before publishing it. As such, this book is of interest to computer scientists and researchers, news and media professionals, as well as policymakers and data-savvy media consumers.

"Digital Preservation Technology for Cultural Heritage" discusses the technology and processes in digital preservation of cultural heritage. It covers topics in five major areas: Digitization of cultural heritage; Digital management in the cultural heritage preservation; Restoration techniques for rigid solid relics; Restoration techniques for paintings; Digital museum. It also includes application examples for digital preservation of cultural heritage.

The book is intended for researchers, advanced undergraduate and graduate students in Computer Graphics and Image Processing as well as Cultural heritage preservation.

Mingquan Zhou is a professor at the College of Information Science and Technology, Beijing Normal University, China. Guohua Geng is a professor at the College of Information Science and Technology, Northwest University, Xi'an, China. Zhongke Wu is a professor at the College of Information Science and Technology, Beijing Normal University, China.

This book collects a number of papers presented at the International Conference on Sensing and Imaging, which was held at Chengdu University of Information Technology on June 5-7, 2017. Sensing and imaging is an interdisciplinary field covering a variety of sciences and techniques such as optics, electricity, magnetism, heat, sound, mathematics, and computing technology. The field has diverse applications of interest such as sensing techniques, imaging, and image processing techniques. This book will appeal to professionals and researchers within the field.

Digital forensics deals with the acquisition, preservation, examination, analysis and presentation of electronic evidence. Computer networks, cloud computing, smartphones, embedded devices and the Internet of Things have expanded the role of digital forensics beyond traditional computer crime investigations. Practically every crime now involves some aspect of digital evidence; digital forensics provides the techniques and tools to articulate this evidence in legal proceedings. Digital forensics also has myriad intelligence applications; furthermore, it has a vital role in cyber security -- investigations of security breaches yield valuable information that can be used to design more secure and resilient systems. Advances in Digital Forensics XV describes original research results and innovative applications in the discipline of digital forensics. In addition, it highlights some of the major technical and legal issues related to digital evidence and electronic crime investigations. The areas of coverage include: forensic models, mobile and embedded device forensics, filesystem forensics, image forensics, and forensic techniques. This book is the fifteenth volume in the annual series produced by the International Federation for Information Processing (IFIP) Working Group 11.9 on Digital Forensics, an international community of scientists, engineers and practitioners dedicated to advancing the state of the art of research and practice in digital forensics. The book contains a selection of fourteen edited papers from the Fifteenth Annual IFIP WG 11.9 International Conference on Digital Forensics, held in Orlando, Florida, USA in the winter of 2019. Advances in Digital Forensics XV is an important resource for researchers, faculty members and graduate students, as well as for practitioners and individuals engaged in research and development efforts for the law enforcement and intelligence communities.

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.

An Image Processing Tour of College Mathematics aims to provide meaningful context for reviewing key topics of the college mathematics curriculum, to help students gain confidence in using concepts and techniques of applied mathematics, to increase student awareness of recent developments in mathematical sciences, and to help students prepare for graduate studies. The topics covered include a library of elementary functions, basic concepts of descriptive statistics, probability distributions of functions of random variables, definitions and concepts behind first- and second-order derivatives, most concepts and techniques of traditional linear algebra courses, an introduction to Fourier analysis, and a variety of discrete wavelet transforms - all of that in the context of digital image processing. Features Pre-calculus material and basic concepts of descriptive statistics are reviewed in the context of image processing in the spatial domain. Key concepts of linear algebra are reviewed both in the context of fundamental operations with digital images and in the more advanced context of discrete wavelet transforms. Some of the key concepts of probability theory are reviewed in the context of image equalization and histogram matching. The convolution operation is introduced painlessly and naturally in the context of naive filtering for denoising and is subsequently used for edge detection and image restoration. An accessible elementary introduction to Fourier analysis is provided in the context of image restoration. Discrete wavelet transforms are introduced in the context of image compression, and the readers become more aware of some of the recent developments in applied mathematics. This text helps students of mathematics ease their way into mastering the basics of scientific computer programming.

With annual gross sales surpassing 100 billion U.S. dollars each of the last two years, the digital games industry may one day challenge theatrical-release movies as the highest-grossing entertainment media in the world. In their examination of the tremendous cultural influence of digital games, Daniel Reardon and David Wright analyze three companies that have shaped the industry: Bethesda, located in Rockville, Maryland, USA; BioWare in Edmonton, Alberta, Canada; and CD Projekt Red in Warsaw, Poland. Each company has used social media and technical content in the games to promote players' belief that players control the companies' game narratives. The result has been at times explosive, as empowered players often attempted to co-op the creative processes of games through discussion board forum demands, fund-raising campaigns to persuade companies to change or add game content, and modifications ("modding") of the games through fan-created downloads. The result has changed the way we understand the interactive nature of digital games and the power of fan culture to shape those games.