• DocumentCode
    1367360
  • Title

    A contour-based recovery of image flow: Iterative transformation method

  • Author

    Wohn, Kwang Yoen ; Wu, Jian ; Brockett, Roger W.

  • Author_Institution
    Dept. of Comput. Inf. Sci., Pennsylvania Univ., Philadelphia, PA, USA
  • Volume
    13
  • Issue
    8
  • fYear
    1991
  • fDate
    8/1/1991 12:00:00 AM
  • Firstpage
    746
  • Lastpage
    760
  • Abstract
    The authors present an iterative algorithm for the recovery of 2-D motion, i.e., for the determination of a transformation that maps one image onto another. The local ambiguity in measuring the motion of contour segments (the aperture problem) implies a reliance on measurements along the normal direction. Since the measured normal flow does not agree with the actual normal flow, the full flow recovered from this erroneous flow also possesses substantial error, and any attempt to recover the 3-D motion from such full flow fails. The proposed method is based on the observation that a polynomial approximation of the image flow provides sufficient information for 3-D motion computation. The use of an explicit flow model results in improved normal flow estimates through an iterative process. The authors discuss the adequacy and the convergence of the algorithm. The algorithm was tested on some synthetic and some simple natural time-varying images. The image flow recovered from this scheme is sufficiently accurate to be useful in 3-D structure and motion computation
  • Keywords
    iterative methods; picture processing; polynomials; 2-D motion; 3-D motion; Iterative transformation method; aperture problem; contour-based recovery; convergence; image flow; local ambiguity; natural time-varying images; polynomial approximation; synthetic images; Fluid flow measurement; Force measurement; Image analysis; Image motion analysis; Image segmentation; Iterative algorithms; Iterative methods; Motion analysis; Motion estimation; Motion measurement;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.85666
  • Filename
    85666