Dense Image Correspondences for Computer Vision

Nonfiction, Computers, Application Software, Computer Graphics, Science & Nature, Technology, Electronics
Cover of the book Dense Image Correspondences for Computer Vision by , Springer International Publishing
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Author: ISBN: 9783319230481
Publisher: Springer International Publishing Publication: November 21, 2015
Imprint: Springer Language: English
Author:
ISBN: 9783319230481
Publisher: Springer International Publishing
Publication: November 21, 2015
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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