rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Dense Image Correspondences for Computer Vision
  name:
    name part: Hassner, Tal.
    role:
      role term: editor.
  name:
    name part: Liu, Ce.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    edition: 1st ed. 2016.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XII, 295 p. 152 illus., 146 illus. in color. online resource.
  abstract: 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 .
  table of contents: 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.
  note: edited by Tal Hassner, Ce Liu.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Image processing
  subject:
    topic: Electrical engineering
  subject:
    topic: Engineering
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Communications Engineering, Networks
  classification: TK5102.9
  classification: TA1637-1638
  classification: TK7882.S65
  classification: 621.382
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319230481
  identifier: http://dx.doi.org/10.1007/978-3-319-23048-1
  record info:
    record creation date: 151121
    record change date: 20151121085728.0
    record identifier: 978-3-319-23048-1