Understanding Vision: Theory, Models, and Data

Li Zhaoping

ISBN 10: 0198829361 / ISBN 13: 9780198829362

Published by Oxford University Press, 2018

About this Item

Leichte Rillen / Abschürfungen / Risse / Knicke; Gebrochener Buchrücken. The field of vision science has expanded significantly over the past thirty years, yet few comprehensive resources guide readers in adopting a computational approach to visual perception and the brain's underlying mechanisms. This book elucidates the computational principles and models of biological visual processing, particularly in primate vision. It is designed for vision scientists who may not be well-versed in mathematical details, allowing them to grasp theoretical principles and their connections to physiological, anatomical, and psychological observations without delving into complex mathematics. For those with a background in physical sciences, especially machine vision, it serves as an analytical introduction to biological vision. It can function as a textbook or reference for vision or computational neuroscience courses aimed at graduate or advanced undergraduate students, and is also suitable for self-learners. Readers can focus on specific chapters, such as Chapter 2 on experimental observations, Chapter 3 on visual input encoding, Chapter 5 on sensory-driven visual attentional selection, and Chapter 6 on visual perception or decoding. With numerous examples illustrating the application of computational principles to experimental findings, this resource is invaluable for students and researchers in computational neuroscience, vision science, machine vision, and physicists interested in visual processes.

Seller Inventory # 6c3a17af-96d5-47c4-99b5-1f5e4464bf07

Bibliographic Details

• Title: Understanding Vision: Theory, Models, and Data
• Publisher: Oxford University Press
• Publication Date: 2018
• Language: English
• ISBN 10: 0198829361
• ISBN 13: 9780198829362
• Binding: Softcover
• Condition: Fine

About this title

Synopsis

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.

About the Author

Li Zhaoping obtained her Ph.D. in physics in 1989 from the California Institute of Technology. In 1998, she helped to found the Gatsby Computational Neuroscience Unit in University College London, where she is currently a professor in Computational Neuroscience in its computer science department.