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Chapman & Hall/CRC Computer Vision

About the Series

This series aims to capture the latest developments in Computer Vision (CV), as well as high-quality work summarizing or contributing to more established topics.

It welcomes contributions from across the spectrum of academic and industrial research and practice, and publishes a broad range of textbooks, reference books, handbooks, and primers.

Topics may include but are not limited to the following: CV applications and systems, 3D, action and behavior recognition, adversarial learning, autonomous vehicles, biometrics, bodies and faces, computational photography, detection and localization, efficient training and inference methods, explainable AI for CV, ethics and responsibility in CV, egocentric vision, image and video manipulation, machine learning for CV, medical and biological CV, motion and tracking, neural generative models, physics-based vision,retrieval and synthesis, robotics and embodied vision, scene analysis and understanding, text and document understanding, unsupervised learning, stereo, transfer learning, video analysis and understanding, vision and other modalities, visual reasoning and logical representation.

For more information or to submit a book proposal for the series, please contact Elliott Morsia, Editor, CS ([email protected]).

2 Series Titles

Per Page

Computer Vision Challenges, Trends, and Opportunities

Computer Vision: Challenges, Trends, and Opportunities

1st Edition


Edited By Md Atiqur Rahman Ahad, Upal Mahbub, Matthew A. Turk, Richard Hartley
June 28, 2024

Computer vision has made enormous progress in recent years, and its applications are multifaceted and growing quickly, while many challenges still remain. This book brings together a range of leading researchers to examine a wide variety of challenges and prospects for computer vision and its ...

Low-Power Computer Vision Improve the Efficiency of Artificial Intelligence

Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence

1st Edition

Edited By George K. Thiruvathukal, Yung-Hsiang Lu, Jaeyoun Kim, Yiran Chen, Bo Chen
February 23, 2022

Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions ...

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