Skip to main content
SpringerLink
Log in

Dense Image Correspondences for Computer Vision

  • Book
  • © 2016

Overview

Editors:
  1. Tal Hassner

    1. The Open University of Israel, Raanana, Israel

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Ce Liu

    1. Google Research, Cambridge, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • 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

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (12 chapters)

  1. Front Matter

    Pages i-xii
    Download chapter PDF

  2. Establishing Dense Correspondences

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Introduction to Dense Optical Flow

      • Ce Liu
      Pages 3-14

    3. SIFT Flow: Dense Correspondence Across Scenes and Its Applications

      • Ce Liu, Jenny Yuen, Antonio Torralba
      Pages 15-49

    4. Dense, Scale-Less Descriptors

      • Tal Hassner, Viki Mayzels, Lihi Zelnik-Manor
      Pages 51-70

    5. Scale-Space SIFT Flow

      • Weichao Qiu, Xinggang Wang, Xiang Bai, Alan Yuille, Zhuowen Tu
      Pages 71-82

    6. Dense Segmentation-Aware Descriptors

      • Eduard Trulls, Iasonas Kokkinos, Alberto Sanfeliu, Francesc Moreno-Noguer
      Pages 83-107

    7. SIFTpack: A Compact Representation for Efficient SIFT Matching

      • Alexandra Gilinsky, Lihi Zelnik-Manor
      Pages 109-133

    8. In Defense of Gradient-Based Alignment on Densely Sampled Sparse Features

      • Hilton Bristow, Simon Lucey
      Pages 135-152

  3. Dense Correspondences and Their Applications

    1. Front Matter

      Pages 153-153
      Download chapter PDF

    2. From Images to Depths and Back

      • Tal Hassner, Ronen Basri
      Pages 155-172

    3. Depth Transfer: Depth Extraction from Videos Using Nonparametric Sampling

      • Kevin Karsch, Ce Liu, Sing Bing Kang
      Pages 173-205

    4. Nonparametric Scene Parsing via Label Transfer

      • Ce Liu, Jenny Yuen, Antonio Torralba
      Pages 207-236

    5. Joint Inference in Weakly-Annotated Image Datasets via Dense Correspondence

      • Michael Rubinstein, Ce Liu, William T. Freeman
      Pages 237-278

    6. Dense Correspondences and Ancient Texts

      • Tal Hassner, Lior Wolf, Nachum Dershowitz, Gil Sadeh, Daniel Stökl Ben-Ezra
      Pages 279-295
Back to top

Keywords

About this book

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.

Editors and Affiliations

  • The Open University of Israel, Raanana, Israel

    Tal Hassner

  • Google Research, Cambridge, USA

    Ce Liu

About the editors

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.

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation