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Occlusion Removal in Video Microscopy

  • Brian Eastwood
  • Russell M. TaylorII
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)

Abstract

Video microscopy offers researchers a method to observe small-scale dynamic processes. It is often useful to remove unchanging portions of image sequences to improve the perception and analysis of moving features. We propose two processing methods (a local method and a global method) for detecting and removing partial stationary occlusions from video microscopy data using the bright-field microscope image model. In both techniques, we compute the relative light transmission across the image plane due to fixed, partially-transparent objects. The resulting transmission map enables reconstruction of a video in which the occlusions have been removed. We present experimental results that compare the effectiveness and applicability of our two approaches.

Keywords

video processing image enhancement occlusion removal light microscopy 

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References

  1. 1.
    Tsin, Y., Ramesh, V., Kanade, T.: Statistical calibration of CCD imaging process. IEEE ICCV 01, 480 (2001)Google Scholar
  2. 2.
    Inoué, S., Spring, K.R.: Video Microscopy: The Fundamentals, 2nd edn. Springer, Heidelberg (1997)Google Scholar
  3. 3.
    Hill, D.B., Plaza, M.J., Bonin, K., Holzwarth, G.: Fast vesicle transport in pc12 neurites: velocities and forces. European Biophysics Journal 33(7), 623–632 (2004)CrossRefGoogle Scholar
  4. 4.
    Schechner, Y.Y., Nayar, S.K.: Generalized mosaicing: High dynamic range in a wide field of view. IJCV 53(3), 245–267 (2003)CrossRefGoogle Scholar
  5. 5.
    Shizawa, M., Mase, K.: Simultaneous multiple optical flow estimation. ICPR 1, 274–278 (1990)Google Scholar
  6. 6.
    Sun, J., Li, Y., Kang, S.B., Shum, H.Y.: Symmetric stereo matching for occlusion handling. IEEE CVPR 2, 399–406 (2005)Google Scholar
  7. 7.
    Jia, J., Wu, T.P., Tai, Y.W., Tang, C.K.: Video repairing: Inference of foreground and background under severe occlusion. IEEE CVPR 01, 364–371 (2004)Google Scholar
  8. 8.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1992)Google Scholar
  9. 9.
    Ibanez, L., Schroeder, W., Ng, L., Cates, J.: The ITK Software Guide. Kitware, Inc., 2nd edn. (2005), ISBN 1-930934-15-7 http://www.itk.org/ItkSoftwareGuide.pdf
  10. 10.
    Frankot, R.T., Chellappa, R.: A method for enforcing integrability in shape from shading algorithms. IEEE PAMI 10(4), 439–451 (1988)zbMATHGoogle Scholar
  11. 11.
    Harris, F.J.: On the use of windows for harmonic analysis with the discrete fourier transform. Proceedings of the IEEE 66(1), 51–83 (1978)CrossRefGoogle Scholar
  12. 12.
    Bovik, A.: Handbook of image and video processing, 2nd edn. Academic Press, San Diego (2005)Google Scholar
  13. 13.
    Antonini, M., Barlaud, M., Mathieu, P., Daubechies, I.: Image coding using wavelet transform. IEEE TIP 1, 205–220 (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Brian Eastwood
    • 1
  • Russell M. TaylorII
    • 1
  1. 1.University of North Carolina at Chapel Hill, Department of Computer Science, Chapel Hill, NC, 27599-3175USA

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