Computer Vision Metrics: Survey, Taxonomy, and Analysis of Computer Vision, Visual Neuroscience, and Visual AI

Krig, Scott

ISBN 10: 9819933927 / ISBN 13: 9789819933921

Published by Springer, 2025

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Bibliographic Details

• Title: Computer Vision Metrics: Survey, Taxonomy, and Analysis of Computer Vision, Visual Neuroscience, and Visual AI
• Publisher: Springer
• Publication Date: 2025
• Language: English
• ISBN 10: 9819933927
• ISBN 13: 9789819933921
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: New

About this title

Synopsis

This 2nd Edition, based on the successful 2016 textbook, has been updated and expanded to cover 3rd generation Computer Vision and AI as it supersedes historical visual computing methods, providing a comprehensive survey of essential topics and methods in Computer Vision. With over 1,200 essential references, as well as chapter-by-chapter learning assignments, the book offers a valuable resource for students, researchers, scientists and engineers, helping them dig deeper into core computer vision and foundational visual computing and neuroscience topics. 

As before, a historical survey of advances in Computer Vision is provided, updated to reflect the latest methods such as Vision Transformers, attention models, alternative features such as Fourier neurons and Binary neurons, hybrid DNN architectures, self-supervised and enhanced learning models, Associative Multimodal Learning, Continuous Learning, View Synthesis, intelligent Scientific Imaging, andadvances in training protocols. Updates have also been added for 2d/3d cameras, software libraries and open source resources, computer vision cloud services, and vision/AI hardware accelerators. Discussion and analysis are provided to uncover intuition and delve into the essence of key advancements, applied and forward-looking topics. 

About the Author

Scott Krig is a pioneer in computer imaging, computer vision, and graphics visualization. He founded Krig Research in 1988, providing the world’s first image and vision systems based on high-performance engineering workstations, super-computers, and dedicated hardware, with optimized computer vision and imaging software libraries for a wide range of applications, serving customers in 25 countries around the globe. Scott is also the author of Synthetic Vision Using Volume Learning and Visual DNA, which presents a multi-dimensional and multivariate feature learning approach to computer vision, intended as the basis for a public Visual Genome Project to catalog all (or nearly all) visual features composing visual objects. Scott studied at Stanford and is the author of patent applications worldwide in various fields, including imaging, computer vision, embedded systems, DRM and computer security.