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Pseudo-real Image Sequence Generator for Optical Flow Computations

  • Vladimír Ulman
  • Jan Hubený
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

The availability of ground-truth flow field is crucial for quantitative evaluation of any optical flow computation method. The fidelity of test data is also important when artificially generated. Therefore, we generated an artificial flow field together with an artificial image sequence based on real-world sample image. The presented framework benefits of a two-layered approach in which user-selected foreground was locally moved and inserted into an artificially generated background. The background is visually similar to input sample image while the foreground is extracted from original and so is the same. The framework is capable of generating 2D and 3D image sequences of arbitrary length. Several examples of the version tuned to simulate real fluorescent microscope images are presented. We also provide a brief discussion.

Keywords

Root Mean Square Image Sequence Sample Image Foreground Object Mask Image 
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.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  2. 2.
    Cédras, C., Shah, M.A.: Motion based recognition: A survey. Image and Vision Computing 13(2), 129–155 (1995)CrossRefGoogle Scholar
  3. 3.
    Gerlich, D., Mattes, J., Eils, R.: Quantitative motion analysis and visualization of cellular structures. Methods 29(1), 3–13 (2003)CrossRefGoogle Scholar
  4. 4.
    Eils, R., Athale, C.: Computational imaging in cell biology. The Journal of Cell Biology 161, 447–481 (2003)CrossRefGoogle Scholar
  5. 5.
    Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. Int. J. Comput. Vision 12(1), 43–77 (1994)CrossRefGoogle Scholar
  6. 6.
    Webb, D., Hamilton, M.A., Harkin, G.J., Lawrence, S., Camper, A.K., Lewandowski, Z.: Assessing technician effects when extracting quantities from microscope images. Journal of Microbiological Methods 53(1), 97–106 (2003)CrossRefGoogle Scholar
  7. 7.
    Galvin, B., McCane, B., Novins, K., Mason, D., Mills, S.: Recovering motion fields: An evaluation of eight optical flow algorithms. In: Proc. of the 9th British Mach. Vis. Conf (BMVC ’98), vol. 1, pp. 195–204 (1998)Google Scholar
  8. 8.
    Beauchemin, S.S., Barron, J.L.: The computation of optical flow. ACM Comput. Surv. 27(3), 433–466 (1995)CrossRefGoogle Scholar
  9. 9.
    Lehmussola, A., Selinummi, J., Ruusuvuori, P., Niemisto, A., Yli-Harja, O.: Simulating fluorescent microscope images of cell populations. In: IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 3153–3156 (2005)Google Scholar
  10. 10.
    Young, I.: Quantitative microscopy. IEEE Engineering in Medicine and Biology Magazine 15(1), 59–66 (1996)CrossRefGoogle Scholar
  11. 11.
    Lin, T., Barron, J.: Image reconstruction error for optical flow. In: Vision Interface, pp. 73–80 (1994)Google Scholar
  12. 12.
    Hubený, J., Matula, P.: Fast and robust segmentation of low contrast biomedical images. In: Proceedings of the Sixth IASTED International Conference VIIP, p. 8 (2006)Google Scholar
  13. 13.
    Saito, T., Toriwaki, J.I.: New algorithms for Euclidean distance transformations of an n-dimensional digitized picture with applications. Pattern Recognition 27, 1551–1565 (1994)CrossRefGoogle Scholar
  14. 14.
    Ulman, V.: Mosaicking of high-resolution biomedical images acquired from wide-field optical microscope. In: EMBEC’05: Proceedings of the 3rd European Medical & Biological Engineering Conference, vol. 11 (2005)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Vladimír Ulman
    • 1
  • Jan Hubený
    • 1
  1. 1.Centre for Biomedical Image Analysis, Masaryk University, Brno 621 00Czech Republic

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