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: Structure from Motion using the Extended Kalman Filter
  name:
    name part: Civera, Javier.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Davison, Andrew J.
    role:
      role term: author.
  name:
    name part: Martínez Montiel, José María.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 172 p. online resource.
  abstract: The fully automated estimation of the 6 degrees of freedom camera motion and the imaged 3D scenario using as the only input the pictures taken by the camera has been a long term aim in the computer vision community. The associated line of research has been known as Structure from Motion (SfM). An intense research effort during the latest decades has produced spectacular advances; the topic has reached a consistent state of maturity and most of its aspects are well known nowadays. 3D vision has immediate applications in many and diverse fields like robotics, videogames and augmented reality; and technological transfer is starting to be a reality. This book describes one of the first systems for sparse point-based 3D reconstruction and egomotion estimation from an image sequence; able to run in real-time at video frame rate and assuming quite weak prior knowledge about camera calibration, motion or scene. Its chapters unify the current perspectives of the robotics and computer vision communities on the 3D vision topic: As usual in robotics sensing, the explicit estimation and propagation of the uncertainty hold a central role in the sequential video processing and is shown to boost the efficiency and performance of the 3D estimation. On the other hand, some of the most relevant topics discussed in SfM by the computer vision scientists are addressed under this probabilistic filtering scheme; namely projective models, spurious rejection, model selection and self-calibration.
  table of contents: Introduction -- Points at Infinity. Mosaics using the Extended Kalman Filter -- Inverse Depth Parametrization -- 1-Point RANSAC -- Degenerate Camera Motions and Model Selection -- Self-calibration -- Conclusions.
  note: by Javier Civera, Andrew J. Davison, José María Martínez Montiel.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Image processing
  subject:
    topic: Robotics
  subject:
    topic: Automation
  subject:
    topic: Engineering
  subject:
    topic: Robotics and Automation
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Image Processing and Computer Vision
  classification: TJ210.2-211.495
  classification: T59.5
  classification: 629.892
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Tracts in Advanced Robotics, 75
  identifier: 9783642248344
  identifier: http://dx.doi.org/10.1007/978-3-642-24834-4
  record info:
    record creation date: 111108
    record change date: 20151120220630.0
    record identifier: 978-3-642-24834-4