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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010304383 | BF241 S77 2012 | Open Access Book | Book | Searching... |
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Summary
Summary
An engaging introduction to the science of vision that offers a coherent account of vision based on general information processing principles In this accessible and engaging introduction to modern vision science, James Stone uses visual illusions to explore how the brain sees the world. Understanding vision, Stone argues, is not simply a question of knowing which neurons respond to particular visual features, but also requires a computational theory of vision. Stone draws together results from David Marr's computational framework, Barlow's efficient coding hypothesis, Bayesian inference, Shannon's information theory, and signal processing to construct a coherent account of vision that explains not only how the brain is fooled by particular visual illusions, but also why any biological or computer vision system should also be fooled by these illusions.
This short text includes chapters on the eye and its evolution, how and why visual neurons from different species encode the retinal image in the same way, how information theory explains color aftereffects, how different visual cues provide depth information, how the imperfect visual information received by the eye and brain can be rescued by Bayesian inference, how different brain regions process visual information, and the bizarre perceptual consequences that result from damage to these brain regions. The tutorial style emphasizes key conceptual insights, rather than mathematical details, making the book accessible to the nonscientist and suitable for undergraduate or postgraduate study.
Author Notes
James V. Stone is a Reader in the Psychology Department of the University of Sheffield. He is coauthor (with John P. Frisby) of the widely used text Seeing- The Computational Approach to Biological Vision (second edition, MIT Press, 2010), and author of Independent Component Analysis- A Tutorial Introduction (MIT Press, 2004).
Reviews 1
Choice Review
Those who are curious about how perception works--specifically, about how the nervous system uses the rather poor quality two-dimensional images in the eyes to produce the three-dimensional, colorful, dynamic, and beautiful experience known as vision--will appreciate this book. Stone (psychology, Univ. of Sheffield, UK) provides a briefer, less technical introduction to computational visual science than he did in Seeing: The Computational Approach to Biological Vision (2nd ed., CH, Mar'11, 48-3852), which he coauthored with J. P. Frisby. Stone retains the rigor and clarity of writing that characterizes the earlier work. The research he describes here shows how such features as contour orientation, motion, contrast, texture, color, brightness, and depth can be analyzed somewhat independently. The reader will learn not only about visual science but also much about theoretical and empirical neuroscience in general. Some topics--e.g., Bayes' theorem, push-pull amplification, and Fourier analysis--could have been expanded, but if this brevity tempts readers to delve a little deeper, that would be a good thing. The book is richly illustrated, and the illustrations are extremely helpful and often surprising and entertaining. Summing Up: Highly recommended. All readers. R. H. Cormack New Mexico Institute of Mining and Technology
