Computer Vision Metrics (Paperback)

Scott Krig

ISBN 10: 1430259299 / ISBN 13: 9781430259299

Published by Springer-verlag Berlin And Heidelberg Gmbh & Co. Kg, Berkley, 2014

About this Item

Paperback. Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing how-to source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners. Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781430259299

Bibliographic Details

• Title: Computer Vision Metrics (Paperback)
• Publisher: Springer-verlag Berlin And Heidelberg Gmbh & Co. Kg, Berkley
• Publication Date: 2014
• Language: English
• ISBN 10: 1430259299
• ISBN 13: 9781430259299
• Binding: Paperback
• Edition: 1st Edition
• Condition: new

About this title

Synopsis

Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing ‘how-to’ source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners.

About the Author

Scott Krig is a pioneer in computer imaging, computer vision, and graphics visualization. He founded Krig Research in 1988 (krigresearch.com), providing the world’s first imaging and vision systems based onhigh-performance engineering workstations, super-computers, and dedicated imaging hardware, serving customers worldwide in 25 countries. Scott has provided imaging and vision solutions around the globe, and has worked closely with many industries, including aerospace, military, intelligence, law enforcement, government research, and academic organizations.More recently, Scott has worked for major corporations and startups serving commercial markets, solving problems in the areas of computer vision, imaging, graphics, visualization, robotics, process control, industrial automation, computer security, cryptography, and consumer applications of imaging and machine vision to PCs, laptops, mobile phones, and tablets. Most recently, Scott provided direction for Intel Corporation in the area of depth-sensing and computer vision methods for embedded systems and mobile platforms.Scott is the author of many patent applications worldwide in the areas of embedded systems, imaging, computer vision, DRM, and computer security, and studied at Stanford.