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.
Chapter PDF
Keywords
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
D. Vernon, Machine Vision Prentice-Hall International, London (1991).
D. Vernon and G. Sandini, Parallel Computer Vision — The VIS a VIS System, Ellis Horwood, London (1992).
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).
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).
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).
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).
L. Jacobson and H. Wechsler, “Derivation of optical flow using a spatiotemporal-frequency approach”, Computer Vision, Graphics, and Image Processing, 38, 29–65 (1987).
M.P. Cagigal, L. Vega, P. Prieto, “Object movement characterization from low-light-level images”, Optical Engineering, 33(8), 2810–2812 (1994).
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).
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).
S. A. Mahmoud, “A new technique for velocity estimation of large moving objects”, IEEE Transactions on Signal Processing, 39(3), 741–743 (1991).
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).
P.V.C. Hough, ‘Method and Means for Recognising Complex Patterms’ U.S. Patent 3,069,654, (1962).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vernon, D. (1996). Segmentation in dynamic image sequences by isolation of coherent wave profiles. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015545
Download citation
DOI: https://doi.org/10.1007/BFb0015545
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61122-6
Online ISBN: 978-3-540-49949-7
eBook Packages: Springer Book Archive