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.
