Editors:
Provides in-depth coverage of dense-correspondence estimation
Covers both the breadth and depth of new achievements in dense correspondence estimation and their applications
Includes information for designing computer vision systems which rely on efficient and robust correspondence estimation
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Table of contents (12 chapters)
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Front Matter
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Establishing Dense Correspondences
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Front Matter
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Dense Correspondences and Their Applications
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Front Matter
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About this book
Editors and Affiliations
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The Open University of Israel, Raanana, Israel
Tal Hassner
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Google Research, Cambridge, USA
Ce Liu
About the editors
Bibliographic Information
Book Title: Dense Image Correspondences for Computer Vision
Editors: Tal Hassner, Ce Liu
DOI: https://doi.org/10.1007/978-3-319-23048-1
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-23047-4Published: 02 December 2015
Softcover ISBN: 978-3-319-35914-4Published: 23 August 2016
eBook ISBN: 978-3-319-23048-1Published: 21 November 2015
Edition Number: 1
Number of Pages: XII, 295
Number of Illustrations: 6 b/w illustrations, 146 illustrations in colour
Topics: Signal, Image and Speech Processing, Image Processing and Computer Vision, Artificial Intelligence, Communications Engineering, Networks