Dense Image Correspondences for Computer Vision
Editors: Hassner, Tal, Liu, Ce (Eds.)
Free Preview- 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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- About this book
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This book describes the fundamental building-block of many new computer vision systems: dense and robust correspondence estimation. Dense correspondence estimation techniques are now successfully being used to solve a wide range of computer vision problems, very different from the traditional applications such techniques were originally developed to solve. This book introduces the techniques used for establishing correspondences between challenging image pairs, the novel features used to make these techniques robust, and the many problems dense correspondences are now being used to solve. The book provides information to anyone attempting to utilize dense correspondences in order to solve new or existing computer vision problems. The editors describe how to solve many computer vision problems by using dense correspondence estimation. Finally, it surveys resources, code and data, necessary for expediting the development of effective correspondence-based computer vision systems.
- About the authors
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Prof. Tal Hassner is a faculty member of the Department of Mathematics and Computer Science, The Open University of Israel, Israel. Ce Liu is a Researcher with Google.
- Table of contents (12 chapters)
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Introduction to Dense Optical Flow
Pages 3-14
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SIFT Flow: Dense Correspondence Across Scenes and Its Applications
Pages 15-49
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Dense, Scale-Less Descriptors
Pages 51-70
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Scale-Space SIFT Flow
Pages 71-82
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Dense Segmentation-Aware Descriptors
Pages 83-107
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Table of contents (12 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Dense Image Correspondences for Computer Vision
- Editors
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- Tal Hassner
- Ce Liu
- Copyright
- 2016
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing Switzerland
- eBook ISBN
- 978-3-319-23048-1
- DOI
- 10.1007/978-3-319-23048-1
- Hardcover ISBN
- 978-3-319-23047-4
- Softcover ISBN
- 978-3-319-35914-4
- Edition Number
- 1
- Number of Pages
- XII, 295
- Number of Illustrations
- 6 b/w illustrations, 146 illustrations in colour
- Topics
