Dense Image Correspondences for Computer Vision

  • Tal Hassner
  • Ce Liu

Table of contents

  1. Front Matter
    Pages i-xii
  2. Establishing Dense Correspondences

    1. Front Matter
      Pages 1-1
    2. Ce Liu, Jenny Yuen, Antonio Torralba
      Pages 15-49
    3. Tal Hassner, Viki Mayzels, Lihi Zelnik-Manor
      Pages 51-70
    4. Weichao Qiu, Xinggang Wang, Xiang Bai, Alan Yuille, Zhuowen Tu
      Pages 71-82
    5. Eduard Trulls, Iasonas Kokkinos, Alberto Sanfeliu, Francesc Moreno-Noguer
      Pages 83-107
    6. Alexandra Gilinsky, Lihi Zelnik-Manor
      Pages 109-133
  3. Dense Correspondences and Their Applications

    1. Front Matter
      Pages 153-153
    2. Tal Hassner, Ronen Basri
      Pages 155-172
    3. Kevin Karsch, Ce Liu, Sing Bing Kang
      Pages 173-205
    4. Ce Liu, Jenny Yuen, Antonio Torralba
      Pages 207-236
    5. Michael Rubinstein, Ce Liu, William T. Freeman
      Pages 237-278
    6. Tal Hassner, Lior Wolf, Nachum Dershowitz, Gil Sadeh, Daniel Stökl Ben-Ezra
      Pages 279-295

About this book

Introduction

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

 

Keywords

Annotation Propagation Data Driven Dense Correspondence Estimation Dense Correspondences Dense Pixel Matching Dense SIFT Depth-transfer Example Based Label-transfer SIFT-Flow Scale-less SIFT

Editors and affiliations

  • Tal Hassner
    • 1
  • Ce Liu
    • 2
  1. 1.The Open University of IsraelRaananaIsrael
  2. 2.Google ResearchCambridgeUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-23048-1
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-23047-4
  • Online ISBN 978-3-319-23048-1
  • About this book
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