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This book discusses the design of the new mobility assistive information and communication technologies (ICT) devices for the visually impaired. The book begins with a definition of the space concept, followed by the concept of interaction with a space during mobility and this interaction characteristics. The contributors will then examine the neuro-cognitive basis of space perception for mobility and different theories of space perception. The text presents the existing technologies for space perception (sense recovery with stem and iPS cells, implants, brain plasticity, sensory substitution devices, multi modal technologies, etc.), the newest technologies for mobility assistance design, the way the feedback on environment is conveyed to the end-user. Methods for formative and summative evaluations of the mobility devices will also be discussed. The book concludes with a look to the future trends in research and technology development for mobility assistive information and communication technologies.
The book examines how well we remember what we see. Research in human memory for visual material varies tremendously across the time scales, stimuli, and scenarios of interest. Because of these distinct pursuits, research in the field of 'visual memory' is in practice rather compartmentalized and as such is disseminated across a range of literatures. The Visual World in Memory pulls together this disparate field with a series of chapters, each written by a leading expert, that concisely present the state-of-the-science in all the areas of research. The result is a single source of information that bridges the divides that separate the field as a whole. Each chapter reviews and analyzes current theories and controversies regarding such issues as visual and spatial working memory, memory for visual features, conjunctions, objects, and locations, memory for faces, memory for real-world scenes, memory for visual events, the role of visual memory in motor planning and action, the relationship between visual memory, reference frames, and navigation, and visual imagery. The rigorous discussion and analysis included in each chapter will appeal to established researchers and vision scientists whilst the breadth of the book will make it an ideal companion for students learning about memory.
In this 2nd edition, the following article has been added: Fricker D, Beraneck M, Tagliabue M and Jeffery KJ (2020) Editorial: Coding for Spatial Orientation in Humans and Animals: Behavior, Circuits and Neurons. Front. Neural Circuits 14:619073. doi: 10.3389/fncir.2020.619073
Vols. for 1963- include as pt. 2 of the Jan. issue: Medical subject headings.
Now available in paperback. This revised and updated edition of the definitive resource for experimental psychology offers comprehensive coverage of the latest findings in the field, as well as the most recent contributions in methodology and the explosion of research in neuroscience. Volume One: Sensation and Perception focuses on sensory experience and complex learned perceptions through modalities such as vision, touch, smell, and hearing.
This book constitutes the refereed proceedings of the First International Symposium on Brain, Vision and Artificial Intelligence, BVAI 2005, held in Naples, Italy in October 2005. The 48 revised papers presented together with 6 invited lectures were carefully reviewed and selected from more than 80 submissions for inclusion in the book. The papers are addressed to the following main topics and sub-topics: brain basics - neuroanatomy and physiology, development, plasticity and learning, synaptic, neuronic and neural network modelling; natural vision - visual neurosciences, mechanisms and model systems, visual perception, visual cognition; artificial vision - shape perception, shape analysis and recognition, shape understanding; artificial inteligence - hybrid intelligent systems, agents, and cognitive models.
Learning and Memory: A Comprehensive Reference, Second Edition, Four Volume Set is the authoritative resource for scientists and students interested in all facets of learning and memory. This updated edition includes chapters that reflect the state-of-the-art of research in this area. Coverage of sleep and memory has been significantly expanded, while neuromodulators in memory processing, neurogenesis and epigenetics are also covered in greater detail. New chapters have been included to reflect the massive increase in research into working memory and the educational relevance of memory research. No other reference work covers so wide a territory and in so much depth. Provides the most comprehensive and authoritative resource available on the study of learning and memory and its mechanisms Incorporates the expertise of over 150 outstanding investigators in the field, providing a ‘one-stop’ resource of reputable information from world-leading scholars with easy cross-referencing of related articles to promote understanding and further research Includes further reading for each chapter that helps readers continue their research Includes a glossary of key terms that is helpful for users who are unfamiliar with neuroscience terminology
This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field. In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.