• DocumentCode
    3054272
  • Title

    Autonomous motion vision

  • Author

    Taalebinezhaad, M. Ali

  • Author_Institution
    MIT, Artificial Intelligence Lab., Cambridge, MA, USA
  • fYear
    1992
  • fDate
    30 Aug-3 Sep 1992
  • Firstpage
    232
  • Lastpage
    235
  • Abstract
    Earlier, the author (1991) introduced a direct method called fixation for the recovery of shape and motion in the general case. The method uses neither feature correspondence nor optical flow. Instead, it directly employs the spatio-temporal gradients of images brightnesses. The present paper reports the experimental results of applying some of the fixation algorithms to a sequence of real images where the motion is a combination of translation and rotation. Techniques for autonomous choice of parameters which result in good estimates for important motion parameters are also described
  • Keywords
    computer vision; image sequences; parameter estimation; autonomous motion vision; computer vision; fixation algorithms; images brightnesses; motion parameter estimation; pattern recognition; real image sequences; shape recovery; spatio-temporal gradients; Artificial intelligence; Brightness; Cameras; Computer vision; Equations; Laboratories; Motion estimation; Optical computing; Shape; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 1992. Vol.I. Conference A: Computer Vision and Applications, Proceedings., 11th IAPR International Conference on
  • Conference_Location
    The Hague
  • Print_ISBN
    0-8186-2910-X
  • Type

    conf

  • DOI
    10.1109/ICPR.1992.201548
  • Filename
    201548