While the field of vision science has grown significantly in the
past three decades, there have been few comprehensive books that
showed readers how to adopt a computional approach to understanding
visual perception, along with the underlying mechanisms in the
brain. Understanding Vision explains the computational principles
and models of biological visual processing, and in particular, of
primate vision. The book is written in such a way that vision
scientists, unfamiliar with mathematical details, should be able to
conceptually follow the theoretical principles and their
relationship with physiological, anatomical, and psychological
observations, without going through the more mathematical pages.
For those with a physical science background, especially those from
machine vision, this book serves as an analytical introduction to
biological vision. It can be used as a textbook or a reference book
in a vision course, or a computational neuroscience course for
graduate students or advanced undergraduate students. It is also
suitable for self-learning by motivated readers. in addition, for
those with a focused interest in just one of the topics in the
book, it is feasible to read just the chapter on this topic without
having read or fully comprehended the other chapters. In
particular, Chapter 2 presents a brief overview of experimental
observations on biological vision; Chapter 3 is on encoding of
visual inputs, Chapter 5 is on visual attentional selection driven
by sensory inputs, and Chapter 6 is on visual perception or
decoding. Including many examples that clearly illustrate the
application of computational principles to experimental
observations, Understanding Vision is valuable for students and
researchers in computational neuroscience, vision science, machine
and computer vision, as well as physicists interested in visual
processes.
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