Screen-based media, such as touch-screens, navigation systems and virtual reality applications merge images and operations. They turn viewing first and foremost into using and reflect the turn towards an active role of the image in guiding a user’s action and perception. From professional environments to everyday life multiple configurations of screens organise working routines, structure interaction, and situate users in space both within and beyond the boundaries of the screen. This volume examines the linking of screen, space, and operation in fields such as remote navigation, architecture, medicine, interface design, and film production asking how the interaction with and through screens structures their users’ action and perception.

Neuropsychological Foundations of Conscious Experience retraces the growth of microgenetic theory from its beginnings in neuropsychology and aphasia in relation to other genetic models of cognition. The account of the mind/brain state and subjective time and change is explored in relation to psychoanalytic concepts of the unconscious as well as philosophical ideas on intentionality, subjectivity and truth. In a final chapter, the theory aims to elucidate creative thinking and mystical experience. The primary aim is to aid the reader to see the progression from clinical studies to theoretical speculation in a way that condenses more extensive and technical writings. This book should appeal to both lay and professional readers with an interest in these topics. It is the author's contention that microgenetic theory is the only available and fully coherent account of the mind that covers not only clinical disorders but normal cognition and metapsychological issues relating to an evolutionary brain model. Jason W. Brown, for over 30 years Clinical Professor of Neurology at New York University, is best known for his microgenetic theory. The theory began with observations on language disorders and neuropsychological studies, and was extended to problems in process theory, especially that of time, change and related issues in philosophy of mind. The author lectures widely on various topics and has published 8 books and over 200 articles.

The proceedings of the 2022 edition of the International Symposium of Robotics Research (ISRR) offer a series of peer-reviewed chapters that report on the most recent research results in robotics, in a variety of domains of robotics including robot design, control, robot vision, robot learning, planning, and integrated robot systems. The proceedings entail also invited contributions that offer provocative new ideas, open-ended themes, and new directions for robotics, written by some of the most renown international researchers in robotics. As one of the pioneering symposia in robotics, ISRR has established some of the most fundamental and lasting contributions in the field since 1983. ISRR promotes the development and dissemination of ground-breaking research and technological innovation in robotics useful to society by providing a lively, intimate, forward-looking forum for discussion and debate about the status and future trends of robotics, with emphasis on its potential role to benefit humans.

This LNCS volume contains the papers presented at the 3rd International Conference on Advances in Pattern Recognition (ICAPR 2005) organized in August, 2005 in the beautiful city of Bath, UK.

This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval. The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.

This book offers the first interdisciplinary survey of community research in the humanities and social sciences to consider such diverse disciplines as philosophy, religious studies, anthropology, sociology, disabilities studies, linguistics, communication studies, and film studies. Bringing together leading international experts, the collection of essays critically maps and explores the state of the art in community research, while also developing future perspectives for a cross-disciplinary rethinking of community. Pursuing such a critical, transdisciplinary approach to community, the book argues, can counteract reductive appropriations of the term ‘community’ and, instead, pave the way for a novel assessment of the concept’s complexity. Since community is, above all, a lived practice that shapes people’s everyday lives, the essays also suggest ways of redoing community; they discuss concrete examples of community practice, thereby bridging the gap between scholars and activists working in the field.

Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache