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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.
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