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. |
|