• DocumentCode
    2514775
  • Title

    Direct dynamic motion vision

  • Author

    Heel, Joachim

  • Author_Institution
    Artificial Intelligence Lab., MIT, Cambridge, MA, USA
  • fYear
    1990
  • fDate
    13-18 May 1990
  • Firstpage
    1142
  • Abstract
    A method is presented for estimating the structure of a scene and the motion of an observer relative to the scene from a sequence of images. Two key features distinguish this method from previous solutions to this problem: no computation of optical flow is required, which leads to considerable speedup, and a Kalman filtering algorithm takes advantage of the entire sequence and leads to noise reduction. Dense depth is estimated by the Kalman filter and is obtained from motion by a least-squares method. No assumptions about surface structure or motion are made. Experimental results on real images are presented
  • Keywords
    Kalman filters; computer vision; least squares approximations; Kalman filtering; direct dynamic motion vision; least-squares; noise reduction; Filtering algorithms; Image motion analysis; Kalman filters; Layout; Motion estimation; Noise reduction; Optical computing; Optical filters; Optical noise; Surface structures;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Robotics and Automation, 1990. Proceedings., 1990 IEEE International Conference on
  • Conference_Location
    Cincinnati, OH
  • Print_ISBN
    0-8186-9061-5
  • Type

    conf

  • DOI
    10.1109/ROBOT.1990.126150
  • Filename
    126150