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: Bayesian Approach to Image Interpretation
  name:
    name part: Kopparapu, Sunil K.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Desai, Uday B.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2002
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 127 p. online resource.
  abstract: Bayesian Approach to Image Interpretation will interest anyone working in image interpretation. It is complete in itself and includes background material. This makes it useful for a novice as well as for an expert. It reviews some of the existing probabilistic methods for image interpretation and presents some new results. Additionally, there is extensive bibliography covering references in varied areas. For a researcher in this field, the material on synergistic integration of segmentation and interpretation modules and the Bayesian approach to image interpretation will be beneficial. For a practicing engineer, the procedure for generating knowledge base, selecting initial temperature for the simulated annealing algorithm, and some implementation issues will be valuable. New ideas introduced in the book include: New approach to image interpretation using synergism between the segmentation and the interpretation modules. A new segmentation algorithm based on multiresolution analysis. Novel use of the Bayesian networks (causal networks) for image interpretation. Emphasis on making the interpretation approach less dependent on the knowledge base and hence more reliable by modeling the knowledge base in a probabilistic framework. Useful in both the academic and industrial research worlds, Bayesian Approach to Image Interpretation may also be used as a textbook for a semester course in computer vision or pattern recognition.
  table of contents: Overview -- Background -- MRF Framework For Image Interpretation -- Bayesian Net Approach to Image Interpretation -- Joint Segmentation and Image Interpretation -- Conclusions.
  note: by Sunil K. Kopparapu, Uday B. Desai.
  subject:
    topic: Computer science
  subject:
    topic: Computer communication systems
  subject:
    topic: Computer graphics
  subject:
    topic: Image processing
  subject:
    topic: Computer Science
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Computer Imaging, Vision, Pattern Recognition and Graphics
  subject:
    topic: Computer Graphics
  subject:
    topic: Computer Communication Networks
  classification: TA1637-1638
  classification: TA1634
  classification: 006.6
  classification: 006.37
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: The International Series in Engineering and Computer Science, 616
  identifier: 9780306469961
  identifier: http://dx.doi.org/10.1007/b117231
  record info:
    record creation date: 100301
    record change date: 20151103125517.0
    record identifier: 978-0-306-46996-1