Overview
Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 469)
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Table of contents (12 chapters)
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Front Matter
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Introduction
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Basic Concepts
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Front Matter
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Model for Motion Perception
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Front Matter
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Other Applications of Neural Fields
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Front Matter
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Back Matter
Keywords
About this book
This framework uses dynamic neural fields as a key mathematical concept. The author demonstrates how neural fields can be applied for the analysis of perceptual phenomena and its underlying neural processes. Also, similar principles form a basis for the design of computer vision systems as well as the design of artificially behaving systems. The book discusses in detail the application of this theoretical approach to motion perception and will be of great interest to researchers in vision science, psychophysics, and biological visual systems.
Authors and Affiliations
Bibliographic Information
Book Title: Dynamic Neural Field Theory for Motion Perception
Authors: Martin A. Giese
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4615-5581-0
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1999
Hardcover ISBN: 978-0-7923-8300-0Published: 31 October 1998
Softcover ISBN: 978-1-4613-7553-1Published: 12 October 2012
eBook ISBN: 978-1-4615-5581-0Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XIX, 257
Topics: Complex Systems, Computer Imaging, Vision, Pattern Recognition and Graphics, Neurosciences, Neuropsychology, Statistical Physics and Dynamical Systems