Dense Image Correspondences for Computer Vision

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 applic...

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Bibliographic Details
Other Authors: Hassner, Tal (Editor), Liu, Ce (Editor)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2016, 2016
Edition:1st ed. 2016
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Description
Summary: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.   ·         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 that rely on efficient and robust correspondence estimation  
Physical Description:XII, 295 p. 152 illus., 146 illus. in color online resource
ISBN:9783319230481