John P. Frisby and James V. Stone.
"Seeing is not a new edition but a completely new book, and a unique book—a carefully written, beautifully illustrated text of the computational approach to human vision that will take the reader from first principles to cutting-edge ideas about all levels of the visual process."
Oliver Braddick, Department of Experimental Psychology, University of Oxford.
"It's back! In its first incarnation, this was one of the treasured books of vision, launching a thousand seminars, workshops, and courses on vision. This second edition covers even more than the first but keeps the excitement of the computational and physiological research that was the strength of the original. It's accessible, advanced, great to read, and fabulous for upper-level undergraduate and graduate courses - an absolute winner."
Patrick Cavanagh, Professeur des universites, Universite Paris Descartes, and Research Professor of Psychology, Harvard University.
Review of Seeing in The Quarterly Review of Biology, September 2011
by Heinrich H. Buelthoff and Lewis Leewui Chuang, Max Planck Institute for BioCybernetics, Tubingen, Germany.
This volume is an excellent example of how textbooks ought to be written and classes taught. Like its predecessor, the current edition employs an exquisite selection of visual illusions and illustrations to provoke a strong desire to understand how the visual system works. Having done so, it proceeds to demonstrate, by example, how this can be achieved by formulating visual phenomena as computational problems that have to be solved by the brain.
Many topics are addressed in this work, from lightness perception to familiar object recognition. In contrast to most textbooks on visual perception, this volume is not organized to reﬂect the functional hierarchy of the visual system. Instead, it is speciﬁcally designed to accustom readers to the computational approach. Thus, “high-level” topics such as 3D-shape perception can sometimes be presented before “low- level” topics such as lightness constancy; this is because the former is better suited as an introductory demonstration of how computational theories can be derived from direct observations, while the latter avails itself to the demonstration of more advanced image processing
techniques (i.e., deconvolution).
Readers can be expected to gain a full appreciation for the diverse competencies of the visual system. This is because they are directly engaged in thinking about the problems that the visual system has to deal with and are instructed in how acceptable solutions can be derived. Clearly, this is a more enjoyable learning experience than being presented with a list of factoids about the visual brain. In fact, the experience of reading this book is not unlike the pleasure that some might gain from working through a crossword puzzle. In conclusion, this volume is suitable for general readers as well as vision scientists, regardless of their topical interests. It dispels any misconception that the “[computational] approach has something uniquely to do with computers” (p. 53) and provides a compelling case that a process is best understood by ﬁrst thinking about the computations that it is expected to perform.