This edited book explores the use of technology to enable us to visualise the life sciences in a more meaningful and engaging way. It will enable those interested in visualisation techniques to gain a better understanding of the applications that can be used in visualisation, imaging and analysis, education, engagement and training. The reader will also be able to learn about the use of visualisation techniques and technologies for the historical and forensic settings. The chapters presented in this volume cover such a diverse range of topics, with something for everyone. We present here chapters on 3D visualising novel stent grafts to aid treatment of aortic aneuryms; confocal microscopy constructed vascular models in patient education; 3D patient specific virtual reconstructions in surgery; virtual reality in upper limb rehabilitation in patients with multiple sclerosis and virtual clinical wards. In addition, we present chapters in artificial intelligence in ultrasound guided regional anaesthesia; carpal tunnel release visualisation techniques; visualising for embryology education and artificial intelligence data on bone mechanics. Finally we conclude with chapters on visualising patient communication in a general practice setting; digital facial depictions of people from the past; instructor made cadaveric videos, novel cadaveric techniques for enhancing visualisation of the human body and finally interactive educational videos and screencasts. This book explores the use of technologies from a range of fields to provide engaging and meaningful visual representations of the biomedical sciences. It is therefore an interesting read for researchers, developers and educators who want to learn how visualisation techniques can be used successfully for a variety of purposes, such as educating students or training staff, interacting with patients and biomedical procedures in general.

This book proposes a new approach to handle the problem of limited training data. Common approaches to cope with this problem are to model the shape variability independently across predefined segments or to allow artificial shape variations that cannot be explained through the training data, both of which have their drawbacks. The approach presented uses a local shape prior in each element of the underlying data domain and couples all local shape priors via smoothness constraints. The book provides a sound mathematical foundation in order to embed this new shape prior formulation into the well-known variational image segmentation framework. The new segmentation approach so obtained allows accurate reconstruction of even complex object classes with only a few training shapes at hand.

This book includes the proceedings of the second workshop on recommender systems in fashion and retail (2020), and it aims to present a state-of-the-art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail, and fashion by presenting readers with chapters covering contributions from academic as well as industrial researchers active within this emerging new field. Recommender systems are often used to solve different complex problems in this scenario, such as product recommendations, or size and fit recommendations, and social media-influenced recommendations (outfits worn by influencers).

Biomedical Image Synthesis and Simulation: Methods and Applications presents the basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. The first part of the book introduces and describes the simulation and synthesis methods that were developed and successfully used within the last twenty years, from parametric to deep generative models. The second part gives examples of successful applications of these methods. Both parts together form a book that gives the reader insight into the technical background of image synthesis and how it is used, in the particular disciplines of medical and biomedical imaging. The book ends with several perspectives on the best practices to adopt when validating image synthesis approaches, the crucial role that uncertainty quantification plays in medical image synthesis, and research directions that should be worth exploring in the future.

Combining theoretical and practical aspects of topology, this book provides a comprehensive and self-contained introduction to topological methods for the analysis and visualization of scientific data. Theoretical concepts are presented in a painstaking but intuitive manner, with numerous high-quality color illustrations. Key algorithms for the computation and simplification of topological data representations are described in detail, and their application is carefully demonstrated in a chapter dedicated to concrete use cases. With its fine balance between theory and practice, "Topological Data Analysis for Scientific Visualization" constitutes an appealing introduction to the increasingly important topic of topological data analysis for lecturers, students and researchers.

This book includes original, peer-reviewed research papers from the 12th China Academic Conference on Printing and Packaging (CACPP 2021), held in Beijing, China on November 12-14, 2021. The proceedings cover the recent findings in color science and technology, image processing technology, digital media technology, mechanical and electronic engineering and numerical control, materials and detection, digital process management technology in printing and packaging, and other technologies. As such, the book is of interest to university researchers, R&D engineers and graduate students in the field of graphic arts, packaging, color science, image science, material science, computer science, digital media, network technology, and smart manufacturing technology.

This work presents lines of investigation and scientific achievements of the Ukrainian school of optimization theory and adjacent disciplines. These include the development of approaches to mathematical theories, methodologies, methods, and application systems for the solution of applied problems in economy, finances, energy saving, agriculture, biology, genetics, environmental protection, hardware and software engineering, information protection, decision making, pattern recognition, self-adapting control of complicated objects, personnel training, etc. The methods developed include sequential analysis of variants, nondifferential optimization, stochastic optimization, discrete optimization, mathematical modeling, econometric modeling, solution of extremum problems on graphs, construction of discrete images and combinatorial recognition, etc. Some of these methods became well known in the world's mathematical community and are now known as classic methods.

Virtual Reality has the potential to provide descriptive and practical information for medical training and therapy while relieving the patient or the physician. Multimodal interactions between the user and the virtual environment facilitate the generation of high-fidelity sensory impressions, by using not only visual and auditory, but also kinesthetic, tactile, and even olfactory feedback modalities. On the basis of the existing physiological constraints, Virtual Reality in Medicine derives the technical requirements and design principles of multimodal input devices, displays, and rendering techniques. Resulting from a course taught by the authors, Virtual Reality in Medicine presents examples for surgical training, intra-operative augmentation, and rehabilitation that are already in use as well as those currently in development. It is well suited as introductory material for engineering and computer science students, as well as researchers who want to learn more about basic technologies in the area of virtual reality applied to medicine. It also provides a broad overview to non-engineering students as well as clinical users, who desire to learn more about the current state of the art and future applications of this technology.

Machine Vision Algorithms in Java provides a comprehensive introduction to the algorithms and techniques associated with machine vision systems. The Java programming language is also introduced, with particular reference to its imaging capabilities. The book contains explanations of key machine vision techniques and algorithms, along with the associated Java source code.Special features include: - A complete self-contained treatment of the topics and techniques essential to the understanding and implementation of machine vision.- An introduction to object-oriented programming and to the Java programming language, with particular reference to its imaging capabilities.- Java source code for a wide range of practical image processing and analysis functions.- Readers will be given the opportunity to download a fully functional Java-based visual programming environment for machine vision, available via the WWW. This contains over 200 image processing, manipulation and analysis functions and will enable users to implement many of the ideas covered in this book. - Details relating to the design of a Java-based visual programming environment for machine vision.- An introduction to the Java 2D imaging and Java Advanced Imaging (JAI) APIs- A wide range of illustrative examples.- Practical treatment of the subject matter. This book is aimed at senior undergraduate and postgraduate students in engineering and computer science as well as practitioners in machine vision who may wish to update or expand their knowledge of the subject. The techniques and algorithms of machine vision are expounded in a way that will be understood not only by specialists but also by those who are less familiar with the topic.

Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output. This is by nature a multi-disciplinary research problem, involving computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), etc. Further, VQA is an ambitious undertaking, as it must overcome the challenges of general image understanding and the question-answering task, as well as the difficulties entailed by using large-scale databases with mixed-quality inputs. However, with the advent of deep learning (DL) and driven by the existence of advanced techniques in both CV and NLP and the availability of relevant large-scale datasets, we have recently seen enormous strides in VQA, with more systems and promising results emerging. This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, and promising future directions. Given its scope, it can be used as a textbook on computer vision and natural language processing, especially for researchers and students in the area of visual question answering. It also highlights the key models used in VQA.

As virtual reality expands from the imaginary worlds of science fiction and pervades every corner of everyday life, it is becoming increasingly important for students and professionals alike to understand the diverse aspects of this technology. This book aims to provide a comprehensive guide to the theoretical and practical elements of virtual reality, from the mathematical and technological foundations of virtual worlds to the human factors and the applications that enrich our lives: in the fields of medicine, entertainment, education and others. After providing a brief introduction to the topic, the book describes the kinematic and dynamic mathematical models of virtual worlds. It explores the many ways a computer can track and interpret human movement, then progresses through the modalities that make up a virtual world: visual, acoustic and haptic. It explores the interaction between the actual and virtual environments, as well as design principles of the latter. The book closes with an examination of different applications, focusing on augmented reality as a special case. Though the content is primarily VR-related, it is also relevant for many other fields.

This book is instrumental to building a bridge between scientists and clinicians in the field of spine imaging by introducing state-of-the-art computational methods in the context of clinical applications. Spine imaging via computed tomography, magnetic resonance imaging, and other radiologic imaging modalities, is essential for noninvasively visualizing and assessing spinal pathology. Computational methods support and enhance the physician's ability to utilize these imaging techniques for diagnosis, non-invasive treatment, and intervention in clinical practice. Chapters cover a broad range of topics encompassing radiological imaging modalities, clinical imaging applications for common spine diseases, image processing, computer-aided diagnosis, quantitative analysis, data reconstruction and visualization, statistical modeling, image-guided spine intervention, and robotic surgery. This volume serves a broad audience as contributions were written by both clinicians and researchers, which reflects the intended readership as well, being a potentially comprehensive book for all spine related clinicians, technicians, scientists, and graduate students.

The proceedings of the 4th Stability and Control Processes Conference are focused on modern applied mathematics, stability theory, and control processes. The conference was held in recognition of the 90th birthday of Professor Vladimir Ivanovich Zubov (1930-2000). This selection of papers reflects the wide-ranging nature of V. I. Zubov's work, which included contributions to the development of the qualitative theory of differential equations, the theory of rigid body motion, optimal control theory, and the theory of electromagnetic fields. It helps to advance many aspects of the theory of control systems, including questions of motion stability, nonlinear oscillations in control systems, navigation and reliability of control devices, vibration theory, and quantization of orbits. The disparate applications covered by the book - in mechanical systems, game theory, solid-state physics, socio-economic systems and medical and biological systems, control automata and navigation - are developments from Professor Zubov's in-depth studies on the theory of stability of motion, the theory of automatic control and the theory of the motions of optimal processes. Stability and Control Processes presents research continuing the legacy of V. I. Zubov and updates it with sections focused on intelligence-based control. These proceedings will be of interest to academics, professionals working in industry and researchers alike.

This open access book takes a fresh look at the nature of the digital travel experience, at a time when more and more people are engaged in online social interaction, games, and other virtual experiences essentially involving online visits to other places. It examines whether these experiences can seem real to the virtual traveller and, if so, under what conditions and on what grounds. The book unpacks philosophical theories relevant to the feeling of being somewhere, emphasising the importance of perception and being-in-the-world. Notions of place are outlined, based on work in tourism studies, human geography, and other applied social fields, with an aim to investigate how and when different experiences of place arise for the traveller and how these relate to telepresence - the sense of being there in another place through digital media. Findings from recent empirical studies of digital travel are presented, including a survey from which the characteristics of "digital travellers" are identified. A review of selected interactive design trends and possibilities leads to the conclusion, which draws these strands together and looks to the future of this topical and expanding field.

This book is focused on the challenges of digital transition in building construction, and potential solutions through the use of multi-criteria analysis. It provides clear explanations of proposed approaches from both a theoretical and practical point of view, including augmented reality and user-reporting. New Approaches for Multi-Criteria Analysis in Constructions begins by explaining classic multi-criteria analysis methods, such as Analytic Hierarchy Processes and the Simos-Roy-Figueira method, before moving on to discuss Augmented Reality - Decision Making. This new approach provides a tool to investigate user's perceptions, and utilises an interactive experience of the real-world environment combined with classic methods to provide a large amount of visual information. Finally, a detailed guide to user-reporting is presented. Offering new possibilities for applying multi-criteria analysis in a simpler, faster and more accessible way, this book supports analysis which considers users experience and perception. Architects, engineers, researchers and practitioners will be able to utilise augmented reality environments, multi-criteria analysis and user reporting for the building design choices, supply selection, maintenance strategies, risk and complex performance assessment.

Machine Vision technology is becoming an indispensible part of the manufacturing industry. Biomedical and scientific applications of machine vision and imaging are becoming more and more sophisticated, and new applications continue to emerge. This book gives an overview of ongoing research in machine vision and presents the key issues of scientific and practical interest. A selected board of experts from the US, Japan and Europe provides an insight into some of the latest work done on machine vision systems and appliccations.

This book contains extended versions of papers presented at the international Conference VIPIMAGE 2009 - ECCOMAS Thematic Conference on Computational Vision and Medical Image, that was held at Faculdade de Engenharia da Universidade do Porto, Portugal, from 14th to 16th of October 2009. This conference was the second ECCOMAS thematic conference on computational vision and medical image processing. It covered topics related to image processing and analysis, medical imaging and computational modelling and simulation, considering their multidisciplinary nature. The book collects the state-of-the-art research, methods and new trends on the subject of computational vision and medical image processing contributing to the development of these knowledge areas.

1) Learn how to develop computer vision application algorithms 2) Learn to use software tools for analysis and development 3) Learn underlying processes need for image analysis 4) Learn concepts so that the reader can develop their own algorithms 5) Software tools provided

This book reports the new results of intelligent robot with hand-eye-brain, from the interdisciplinary perspective of information science and neuroscience. It collects novel research ideas on attractive region in environment (ARIE), intrinsic variable preserving manifold learning (IVPML) and biologically inspired visual congnition, which are theoretically important but challenging to develop the intelligent robot. Furthermore, the book offers new thoughts on the possible future development of human-inspired robotics, with vivid illustrations. The book is useful for researchers, R&D engineers and graduate students working on intelligent robots.

Motion-based recognition deals with the recognition of an object and/or its motion, based on motion in a series of images. In this approach, a sequence containing a large number of frames is used to extract motion information. The advantage is that a longer sequence leads to recognition of higher level motions, like walking or running, which consist of a complex and coordinated series of events. Unlike much previous research in motion, this approach does not require explicit reconstruction of shape from the images prior to recognition. This book provides the state-of-the-art in this rapidly developing discipline. It consists of a collection of invited chapters by leading researchers in the world covering various aspects of motion-based recognition including lipreading, gesture recognition, facial expression recognition, gait analysis, cyclic motion detection, and activity recognition. Audience: This volume will be of interest to researchers and post- graduate students whose work involves computer vision, robotics and image processing.

This book summarizes the results of our modeling-from-reality (MFR) project which took place over the last decade or so. The goal of this project is to develop techniques for modeling real objects and/or environments into geometric and photometric models through computer vision techniques. By developing such techniques, time consuming modeling process, currently un dertaken by human programmers, can be (semi-)automatically performed, and, as a result, we can drastically shorten the developing time of such virtual reality systems, reduce their developing cost, and widen their application areas. Originally, we began to develop geometric modeling techniques that acquire shape information of objects/environments for object recognition. Soon, this effort evolved into an independent modeling project, virtual-reality modeling, with the inclusion of photometric modeling aspects that acquire appearance information, such as color, texture, and smoothness. Over the course of this development, it became apparent that environmental modeling techniques were necessary when applying our techniques to mixed realities that seamlessly combine generated virtual models with other real/virtual images. The material in his book covers these aspects of development."

The aim of the conference is to present and discuss new methods, issues and challenges encountered in all parts of the complex process of gradual development and application of digital surface models. This process covers data capture, data generation, storage, model creation, validation, manipulation, utilization and visualization. Each stage requires suitable methods and involves issues that may substantially decrease the value of the model. Furthermore, the conference provides a platform to discuss the requirements, features and research approaches for 3D modeling, continuous field modeling and other geoscience applications. The conference covers the following topics: - LIDAR for elevation data - Radar interferometry for elevation data - Surface model creation - Surface model statistics - Surface model storage (including data formats, standardization, database) - Feature extraction - Analysis of surface models - Surface models for hydrology, meteorology, climatology - Surface models for signal spreading - Surface models for geology (structural, mining) - Surface models for environmental science - Surface models for visibility studies - Surface models for urban geography - Surface models for human geography - Uncertainty of surface models and digital terrain analysis - Surface model visual enhancement and rendering

Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow 'with' and 'without' transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists.

This book addresses and disseminates research and development in the applications of intelligent techniques for computer vision, the field that works on enabling computers to see, identify, and process images in the same way that human vision does, and then providing appropriate output. The book provides contributions which include theory, case studies, and intelligent techniques pertaining to computer vision applications. The book helps readers grasp the essence of the recent advances in this complex field. The audience includes researchers, professionals, practitioners, and students from academia and industry who work in this interdisciplinary field. The authors aim to inspire future research both from theoretical and practical viewpoints to spur further advances in the field.

Recent years have witnessed important advancements in our understanding of the psychological underpinnings of subjective properties of visual information, such as aesthetics, memorability, or induced emotions. Concurrently, computational models of objective visual properties such as semantic labelling and geometric relationships have made significant breakthroughs using the latest achievements in machine learning and large-scale data collection. There has also been limited but important work exploiting these breakthroughs to improve computational modelling of subjective visual properties. The time is ripe to explore how advances in both of these fields of study can be mutually enriching and lead to further progress. This book combines perspectives from psychology and machine learning to showcase a new, unified understanding of how images and videos influence high-level visual perception - particularly interestingness, affective values and emotions, aesthetic values, memorability, novelty, complexity, visual composition and stylistic attributes, and creativity. These human-based metrics are interesting for a very broad range of current applications, ranging from content retrieval and search, storytelling, to targeted advertising, education and learning, and content filtering. Work already exists in the literature that studies the psychological aspects of these notions or investigates potential correlations between two or more of these human concepts. Attempts at building computational models capable of predicting such notions can also be found, using state-of-the-art machine learning techniques. Nevertheless their performance proves that there is still room for improvement, as the tasks are by nature highly challenging and multifaceted, requiring thought on both the psychological implications of the human concepts, as well as their translation to machines.