Images are not neutral conveyors of messages shipped around the globe to achieve globalized spectatorship. They are powerful forces that elicit very diverse responses and can resist new visual hegemonies of our global world. Bringing together case studies from the field of media, art, politics, religion, anthropology and science, this volume breaks new ground by reflecting on the very power of images beyond their medial exploitation. The contributions by Hans Belting, Susan Buck-Morss, Georges Didi-Huberman, W.J.T. Mitchell, and Ticio Escobar among others testify that globalization does not necessarily equal homogenization, and that images can open up alternative ways of picturing what is to come.

This research addresses the problem of acquiring a time series of magnetic resonance images with both high spatial and temporal resolutions. Specifically, we systematically investigate the advantages and limitations of reduced-encoding imaging using a priori constraints. This study reveals that if the available a priori information is a reference image, direct use of this information to 'optimize' data acquisition using the existing wavelet transform or singular value decomposition schemes can undermine the capability to detect new image features. However, proper incorporation of the a priori information in the image reconstruction step can significantly reduce the resolution loss associated with reduced-encoding. For Fourier encoded data, we have shown that the Generalized-Series (GS) model is an effective mathematical framework for carrying out the constrained reconstruction step. Several techniques are proposed in this dissertation to improve the basis functions of the GS model by introducing dynamic information. The two reference reduced-encoding imaging by generalized-series reconstruction (TRIGR) method suppresses background information through the use of a second high resolution reference image. A second technique injects information from the dynamic data into the GS basis functions, as opposed to deriving them solely from the reference information. These techniques allow the GS basis functions to more accurately represent the areas of dynamic change. Finally, motion that occurs between the acquisition of the reference and dynamic data sets can render the reference information useless as a constraint for image reconstruction. A motion compensation method is proposed which uses a similarity norm to accurately detect the motion in spite of contrast changes and the low resolution nature of the dynamic data.

This book systematically discusses the basic concepts, theories, research and latest trends in image fusion. It focuses on three image fusion categories – pixel, feature and decision – presenting various applications, such as medical imaging, remote sensing, night vision, robotics and autonomous vehicles. Further, it introduces readers to a new category: edge-preserving-based image fusion, and provides an overview of image fusion based on machine learning and deep learning. As such, it is a valuable resource for graduate students and scientists in the field of digital image processing and information fusion.

The Unconscious Body Image espouses a completely original view of the links between physical and psychic development, providing fresh insight into our understanding of psychosomatic symptoms and child development. Françoise Dolto describes how unconsciously held mental images of the body and its functioning impact upon the subject’s feelings and ideas of themself, and conversely how emotions and ideas impact upon the body’s functioning by way of these unconscious images. The Unconscious Body Image also presents Dolto’s view of the development of mind in relation to unconscious body images generated at each stage of development (oral, anal, genital, latency and puberty), and ideas about psychic castration at each developmental stage and children’s socialisation, filling a significant gap in psychoanalytic understanding of the mental integration of social law. This book will be a key text for psychoanalysts in practice and in training, particularly those working with children, psychoanalytic psychotherapists and psychodynamic practitioners in the social sciences, childcare and education.

This book combines semi-physical simulation technology with an Internet of Things (IOT) application system based on novel mathematical methods such as the Fisher matrix, artificial neural networks, thermodynamic analysis, support vector machines, and image processing algorithms. The dynamic testing and semi-physical verification of the theory and application were conducted for typical IOT systems such as RFID systems, Internet of Vehicles systems, and two-dimensional barcode recognition systems. The findings presented are of great scientific significance and have wide application potential for solving bottlenecks in the development of RFID technology and IOT engineering. The book is a valuable resource for postgraduate students in fields such as computer science and technology, control science and engineering, and information science. Moreover, it is a useful reference resource for researchers in IOT and RFID-related industries, logistics practitioners, and system integrators.