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Segmentation in dynamic image sequences by isolation of coherent wave profiles

  • David Vernon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1064)

Abstract

A segmentation and velocity estimation technique is presented which treats each object (either moving or stationary) as a distinct intensity wave profile. The Fourier components of wave profiles — and equally of objects — which move with constant velocity exhibit a regular frequency-dependent phase change. Using a Hough transform which embodies the relationship between velocity and phase change, moving objects are isolated by identifying the subset of the Fourier components of the total image intensity wave profile which exhibit this phase relationship. Velocity is measured by locating local maxima in the Hough space and segmentation is effected by re-constituting the moving wave profile — the object — from the Fourier components which satisfy the velocity/phase-change relationship for the detected velocity.

Keywords

Optical Flow Discrete Fourier Transform Fourier Component Wave Profile Hough Transform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    D. Vernon, Machine Vision Prentice-Hall International, London (1991).Google Scholar
  2. 2.
    D. Vernon and G. Sandini, Parallel Computer Vision — The VIS a VIS System, Ellis Horwood, London (1992).Google Scholar
  3. 3.
    J.H. Duncan and T.-C. Chou, “On the detection and the computation of optical flow”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(3), 346–352 (1992).Google Scholar
  4. 4.
    H. Shariat and K.E. Price, “Motion estimation with more than two frames”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(5), 417–434 (1990).Google Scholar
  5. 5.
    M. Otte and H.-H. Nagel, “Optical flow estimation: advances and comparisons”, Lecture Notes in Computer Science, J.O. Eklundh (Ed.), Computer Vision — ECCV '94, Springer-Verlag, Berlin, 51–60 (1994).Google Scholar
  6. 6.
    M. Tistarelli, “Multiple constraints for optical flow”, Lecture Notes in Computer Science, J.O. Eklundh (Ed.), Computer Vision — ECCV '94, Springer-Verlag, Berlin, 61–70 (1994).Google Scholar
  7. 7.
    L. Jacobson and H. Wechsler, “Derivation of optical flow using a spatiotemporal-frequency approach”, Computer Vision, Graphics, and Image Processing, 38, 29–65 (1987).Google Scholar
  8. 8.
    M.P. Cagigal, L. Vega, P. Prieto, “Object movement characterization from low-light-level images”, Optical Engineering, 33(8), 2810–2812 (1994).Google Scholar
  9. 9.
    M.P. Cagigal, L. Vega, P. Prieto, “Movement characterization with the spatiotem-poral Fourier transform of low-light-level images”, Applied Optics, 34(11), 1769–1774 (1995).Google Scholar
  10. 10.
    S. A. Mahmoud, M.S. Afifi, and R. J. Green, “Recognition and velocity computation of large moving objects in images”, IEEE Transactions on Acoustics, Speech, and Signal Processing, 36(11), 1790–1791 (1988).Google Scholar
  11. 11.
    S. A. Mahmoud, “A new technique for velocity estimation of large moving objects”, IEEE Transactions on Signal Processing, 39(3), 741–743 (1991).Google Scholar
  12. 12.
    S.A. Rajala, A. N. Riddle, and W.E. Snyder, “Application of one-dimensional Fourier transform for tracking moving objects in noisy environments”, Computer Vision, Graphics, and Image Processing, 21, 280–293 (1983).Google Scholar
  13. 13.
    P.V.C. Hough, ‘Method and Means for Recognising Complex Patterms’ U.S. Patent 3,069,654, (1962).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • David Vernon
    • 1
  1. 1.Department of Computer ScienceMaynooth CollegeIreland

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