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: On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities
  name:
    name part: Spehr, Jens.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2015
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 199 p. 107 illus., 92 illus. in color. online resource.
  abstract: In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.
  table of contents: Introduction -- Probabilistic Graphical Models -- Hierarchical Graphical Models -- Learning of Hierarchical Models.-Object Recognition -- Human Pose Estimation -- Scene Understanding for Intelligent Vehicles -- Conclusion.
  note: by Jens Spehr.
  subject:
    topic: Engineering
  subject:
    topic: Image processing
  subject:
    topic: Pattern recognition
  subject:
    topic: Computational intelligence
  subject:
    topic: Robotics
  subject:
    topic: Automation
  subject:
    topic: Engineering
  subject:
    topic: Robotics and Automation
  subject:
    topic: Computational Intelligence
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Pattern Recognition
  classification: TJ210.2-211.495
  classification: T59.5
  classification: 629.892
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Systems, Decision and Control, 11
  identifier: 9783319113258
  identifier: http://dx.doi.org/10.1007/978-3-319-11325-8
  record info:
    record creation date: 141113
    record change date: 20151218161455.0
    record identifier: 978-3-319-11325-8