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Multisensor data fusion. --- Data fusion --- Multi-sensor fusion --- Sensor fusion --- Signal processing --- Sensor networks

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Sensor networks. --- Multisensor data fusion. --- Data fusion --- Multi-sensor fusion --- Sensor fusion --- Signal processing --- Sensor networks --- Networks, Sensor --- Detectors --- Context-aware computing --- Multisensor data fusion

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Auditory behavior, perception, and cognition are all shaped by information from other sensory systems. This volume examines this multi-sensory view of auditory function at levels of analysis ranging from the single neuron to neuroimaging in human clinical populations. Visual Influence on Auditory Perception Adrian K.C. Lee and Mark T. Wallace Cue Combination within a Bayesian Framework David Alais and David Burr Toward a Model of Auditory-Visual Speech Intelligibility Ken W. Grant and Joshua G. W. Bernstein An Object-based Interpretation of Audiovisual Processing Adrian K.C. Lee, Ross K. Maddox, and Jennifer K. Bizley Hearing in a “Moving” Visual World: Coordinate Transformations Along the Auditory Pathway Shawn M. Willett, Jennifer M. Groh, Ross K. Maddox Multisensory Processing in the Auditory Cortex Andrew J. King, Amy Hammond-Kenny, Fernando R. Nodal Audiovisual Integration in the Primate Prefrontal Cortex Bethany Plakke and Lizabeth M. Romanski Using Multisensory Integration to Understand Human Auditory Cortex Michael S. Beauchamp Combining Voice and Face Content in the Primate Temporal Lobe Catherine Perrodin and Christopher I. Petkov Neural Network Dynamics and Audiovisual Integration Julian Keil and Daniel Senkowski Cross-Modal Learning in the Auditory System Patrick Bruns and Brigitte Röder Multisensory Processing Differences in Individuals with Autism Spectrum Disorder Sarah H. Baum Miller, Mark T. Wallace Adrian K.C. Lee is Associate Professor in the Department of Speech & Hearing Sciences and the Institute for Learning and Brain Sciences at the University of Washington, Seattle Mark T. Wallace is the Louise B McGavock Endowed Chair and Professor in the Departments of Hearing and Speech Sciences, Psychiatry, Psychology and Director of the Vanderbilt Brain Institute at Vanderbilt University, Nashville Allison B. Coffin is Associate Professor in the Department of Integrative Physiology and Neuroscience at Washington State University, Vancouver, WA Arthur N. Popper is Professor Emeritus and research professor in the Department of Biology at the University of Maryland, College Park Richard R. Fay is Distinguished Research Professor of Psychology at Loyola University, Chicago The chapter “Multisensory Processing in the Auditory Cortex” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Multisensor data fusion. --- Data fusion --- Multi-sensor fusion --- Sensor fusion --- Signal processing --- Sensor networks --- Otorhinolaryngology. --- Neurosciences. --- Neural sciences --- Neurological sciences --- Neuroscience --- Medical sciences --- Nervous system --- Ear, nose, and throat diseases --- ENT diseases --- Otorhinolaryngology --- Medicine

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The application of sensor technology has brought considerable interest in the area of image fusion. Written by leading experts in the field, this book brings together in one volume the most recent algorithms, design techniques and applications in the topical field of image fusion. The applications are drawn from military, medical and civilian areas and give practical advice and pointers to the development of future applications in a variety of fields.This book will be an invaluable resource to R&D engineers, academic researchers and system developers requiring the most up-to-da

Image processing --- Multisensor data fusion. --- Data processing. --- Digital techniques. --- Data fusion --- Multi-sensor fusion --- Sensor fusion --- Signal processing --- Sensor networks --- Digital image processing --- Digital electronics --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing

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Vision sensors have limited fields of views and can only "see" a portion of a scene from a single viewpoint. To make the entire object visible, the sensor has to be moved from one place to another around the object to observe all features of interest, which brings a multiview vision task that has to be solved by means of active perception. The sensor planning presented in this book describes some effective strategies to generate a sequence of viewing poses and sensor settings for optimally completing a perception task. Several methods are proposed to solve the problems in both model-based and nonmodel-based vision tasks. For model-based applications, the method involves determination of the optimal sensor placements and a shortest path through these viewpoints for automatic generation of a perception plan. For nonmodel-based applications, the method involves determination of the best next view and sensor settings, to incrementally acquire the object information and to find geometrical cues to predict the unknown portion of an object or environment. The ten chapters in Active Vision Planning draw on recent work in robot vision over ten years, particularly in the use of new concepts of active sensing, reconfiguration, recalibration, sensor modeling, sensing constraints, sensing evaluation, viewpoint decision, sensor placement graph, model based planning, path planning, planning for robot in unknown environment, dynamic 3D construction, surface prediction, etc. Implementation examples are also provided with theoretical methods for testing in a real robot system. With these optimal sensor planning strategies, this book will give the robot vision system the adaptability needed in many practical applications.

Computer vision. --- Pattern recognition systems. --- Detectors --- Multisensor data fusion. --- Robotics. --- Automation --- Machine theory --- Design. --- Calibration. --- Data fusion --- Multi-sensor fusion --- Sensor fusion --- Signal processing --- Sensor networks --- Sensors --- Engineering instruments --- Physical instruments --- Pattern classification systems --- Pattern recognition computers --- Pattern perception --- Computer vision --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Information Technology --- Artificial Intelligence