• DocumentCode
    1514376
  • Title

    A framework for visual motion understanding

  • Author

    Tsotsos, John K. ; Mylopoulos, John ; Covvey, H.D. ; Zucker, Steven W.

  • Author_Institution
    Cardiovascular Unit, Toronto General Hospital, Toronto, Ont., Canada
  • Issue
    6
  • fYear
    1980
  • Firstpage
    563
  • Lastpage
    573
  • Abstract
    A framework for the abstraction of motion concepts from sequences of images by computer is presented. The framework includes: 1) representation of knowledge for motion concepts that is based on semantic networks; and 2) associated algorithms for recognizing these motion concepts. These algorithms implement a form of feedback by allowing competition and cooperation among local hypotheses. They also allow a change of attention mechanism that is based on similarity links between knowledge units, and a hypothesis ranking scheme based on updating of certainty factors that reflect the hypothesis set inertia. The framework is being realized with a system called ALVEN. The purpose behind this system is to provide an evolving research prototype for experimenting with the analysis of certain classes of biomedical imagery, and for refining and quantifying the body of relevant medical knowledge.
  • Keywords
    artificial intelligence; biomedical engineering; computerised pattern recognition; computerised picture processing; ALVEN; artificial intelligence; biomedical imagery; certainty factors; feedback; hypothesis ranking; knowledge units; local hypotheses; semantic networks; similarity links; visual motion understanding; Biomedical imaging; Knowledge based systems; Motion segmentation; Muscles; Semantics; Shape; Visualization; Artificial intelligence; computer vision; knowledge-based systems; left ventricular wall motion analysis; motion understanding;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/TPAMI.1980.6447704
  • Filename
    6447704