Overview
- Provides the most complete survey of computer vision feature description methods including local, regional, global, and basis feature learning via deep learning and neural networks
- Offers learning assignments at the end of each chapter for student or instructor use
- Includes techniques for optimizing computer vision algorithm performance such as SW and HW architecture considerations
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About this book
To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized.
The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCV and other imaging and deep learning tools.
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Table of contents (10 chapters)
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Bibliographic Information
Book Title: Computer Vision Metrics
Book Subtitle: Textbook Edition
Authors: Scott Krig
DOI: https://doi.org/10.1007/978-3-319-33762-3
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-33761-6Published: 04 October 2016
Softcover ISBN: 978-3-319-81595-4Published: 14 June 2018
eBook ISBN: 978-3-319-33762-3Published: 16 September 2016
Edition Number: 1
Number of Pages: XVIII, 637
Number of Illustrations: 192 b/w illustrations, 139 illustrations in colour
Topics: Artificial Intelligence, Data Mining and Knowledge Discovery, Signal, Image and Speech Processing, Computational Intelligence