In six parts, this book considers the extent to which computational, neural, and ecological constraints have shaped the mechanisms underlying motion vision: - Early Motion Vision - Motion Signals for Local and Global Analysis - Optical Flow Patterns - Motion Vision in Action - Neural Coding of Motion - Motion in Natural Environments Each topic is introduced by a keynote chapter which is accompanied by several companion articles. Written by an international group of experts in neurobiology, psychophysics, animal behaviour, machine vision, and robotics, the book is designed to explore as comprehensively as possible the present state of knowledge concerning the principal factors that have guided the evolution of motion vision.

Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.

This book on autonomous road-following vehicles brings together twenty years of innovation in the field. The book uniquely details an approach to real-time machine vision for the understanding of dynamic scenes, viewed from a moving platform that begins with spatio-temporal representations of motion for hypothesized objects whose parameters are adjusted by well-known prediction error feedback and recursive estimation techniques.

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Motion perception lies at the heart of the scientific study of vision. The motion aftereffect (MAE) is the appearance of directional movement in a stationary object or scene after the viewer has been exposed to viusal motion in the opposite direction. For example, after one has looked at a waterfall for a period of time, the scene beside the waterfall may appear to move upward when one's gaze is transfered to it. Although the phenomenon seems simple, research has revealed copmlexities in the underlying mechanisms, and offered general lessons about how the brain processes visual information. In the 1990s alone, more than 200 papers have been published on MAE, largely inspired by improved techniques for examining brain electrophysiology and by emerging new theories of motion perception.