Dense Image Correspondences for Computer Vision -

Dense Image Correspondences for Computer Vision

Tal Hassner, Ce Liu (Herausgeber)

Buch | Softcover
XII, 295 Seiten
2016 | 1. Softcover reprint of the original 1st ed. 2016
Springer International Publishing (Verlag)
978-3-319-35914-4 (ISBN)
99,98 inkl. MwSt
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.

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.

Introduction to Dense Optical Flow.- SIFT Flow: Dense Correspondence across Scenes and its Applications.- Dense, Scale-Less Descriptors.- Scale-Space SIFT Flow.- Dense Segmentation-aware Descriptors.- SIFTpack: A Compact Representation for Efficient SIFT Matching.- In Defense of Gradient-Based Alignment on Densely Sampled Sparse Features.- From Images to Depths and Back.- DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling.- Joint Inference in Image Datasets via Dense Correspondence.- Dense Correspondences and Ancient Texts.

Erscheinungsdatum
Zusatzinfo XII, 295 p. 152 illus., 146 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 474 g
Themenwelt Technik Elektrotechnik / Energietechnik
Schlagworte Annotation Propagation • Data Driven • Dense Correspondence Estimation • Dense Correspondences • Dense Pixel Matching • Dense SIFT • Depth-transfer • Example Based • Label-transfer • Scale-less SIFT • SIFT-Flow
ISBN-10 3-319-35914-2 / 3319359142
ISBN-13 978-3-319-35914-4 / 9783319359144
Zustand Neuware
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