Overview
Computer Vision Metrics provides an extensive survey and analysis of current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features.
The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more.
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Table of contents (13 chapters)
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Front Matter
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Back Matter
About this book
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
Bibliographic Information
Book Title: Computer Vision Metrics
Book Subtitle: Survey, Taxonomy, and Analysis
Authors: Scott Krig
DOI: https://doi.org/10.1007/978-1-4302-5930-5
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Professional and Applied Computing (R0), Apress Access Books
Copyright Information: Scott Krig 2014
License: CC BY-NC
Softcover ISBN: 978-1-4302-5929-9Published: 30 May 2014
eBook ISBN: 978-1-4302-5930-5Published: 14 June 2014
Edition Number: 1
Number of Pages: XXXI, 508
Number of Illustrations: 216 b/w illustrations
Topics: Computer Graphics, Image Processing and Computer Vision, Natural Language Processing (NLP)