Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Optical Flow Estimation of Large Displacements from Real Sequential Images

  • Kim, Jin-Woo (Department of Information Communication Engineering, Kyungsung University)
  • Received : 2011.05.17
  • Accepted : 2011.06.01
  • Published : 2011.06.30
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Abstract

In computing the optical flow. Horn and Schunck's method which is a representative algorithm is based on differentiation. But it is difficult to estimate the velocity for a large displacement by this algorithm. To cope with this problem multigrid method has been proposed. In this paper, we have proposed a scaled multigrid algorithm which the initial flow for a level is calculated by the summation of the optimally scaled flow and error flow. The optimally scaled flow is the scaled expanded flow of the previous level, which can generate an estimated second image having the least RMS error with respect to the original second image, and the error flow is the flow between the estimated second image (generated by the optimally scaled flow) and the original second image. The flow for this level is then estimated using the original first and second images and the initial flow for that level. From among the various coarsest starting levels of the multigrid algorithm, we select the one that finally gives the best estimated flow. Better results were achieved using our proposed method compared with Horn and Schunck's method and a conventional multigrid algorithm.

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References

  1. S. S. Beauchemin, and J. L. barron, "The computation of optical flow," ACM Comput. Surv. vol. 27, no. 3, pp. 433-466, 1995. https://doi.org/10.1145/212094.212141
  2. D. W. Murray, and B. F. Buxton, "Experiments in the Machine Interpratation of Visual Motion," MIT Press, Cambridge, Massachusetts, Sep. 1990.
  3. H. C. Longuet-Higgins, and K. Prazdny, "The interpretation of a moving retinal image," Proc. Roy. Soc. London B-208, pp. 385-397, 1980. https://doi.org/10.1098/rspb.1980.0057
  4. A. M. Waxman, and S. Ullman, "Surface structure and three dimensional motion from image-flow kinematics," Int. J. Robotics, no. 4, pp. 72-94, Sep. 1985. https://doi.org/10.1177/027836498500400306
  5. A. R. Bruss, and B. K. P. Horn, "Passive navigation," Computer Vision Graphics Image Processing, vol. 21, pp. 3-20, 1983. https://doi.org/10.1016/S0734-189X(83)80026-7
  6. D. Heeger, and A. Jepson, "Simple method for computing 3D motion and depth," Proc. Third Int' Cconf. Computer Vision, osaka, Japan, pp. 96-100, April. 1990.
  7. S. Ullman, "Analysis of vision motion by biological and computer systems," IEEE Trans. Comput, vol. 14, no. , pp. 57-69, 1981.
  8. E. Hildreth, and C. Koch, "The analysis of visual motion: from computational theory to neuronal mechanisms," Ann. Rev. neurosci, pp. 477-533, 1987.
  9. E. Hildreth, "Computations underlying the measurement of visual motion," Artificial Intell, vol. 23, pp. 309-354, 1984. https://doi.org/10.1016/0004-3702(84)90018-3
  10. B. K. P. Horn, and G. Schunck, "Determining optical flow," Artificial Intell, vol.17, pp. 185-203, 1981. https://doi.org/10.1016/0004-3702(81)90024-2
  11. H. Nagel, "Analysis techniques for image sequences," Proc.4th Int. Joint Conf. Patt. Recog, Kyoto, Japan, 1987.
  12. S. Uras, F. Girosi, A. Verri, and V. Torre, "A computational approach to motion perception," Biological Cybernet, vol. 60, pp. 79-87, 1988. https://doi.org/10.1007/BF00202895
  13. A. Brandt, "Multi-level adaptive solutions to boundary value problems," Math. Comput., vol. 31, pp. 333-390,1877.
  14. W. Enkelmann, "Investigation of multigird algorithms for the estimation of optical flow fields in image sequences," Computer Vision Graph Image Processs, vol. 43, pp. 150-177, 1988. https://doi.org/10.1016/0734-189X(88)90059-X
  15. D. Terzopoulos, "Image analysis using multigrid relaxation methods," IEEE Trans. Pattern Anal. Mach. Intell, vol. 8, pp. 129-139, 1986.
  16. R. Battiti, E. Amaldi, and C. Koch, "Computing optical flow asross multiple scales: An adaptive coarse-to-fme strategy," Int. J. Comput. Vision, vol. 6, no. 2, pp. 133-145, 1991. https://doi.org/10.1007/BF00128153
  17. D. Marr, and S. Ullman, "Directional selectivity and its use in early visual processing," Proc. Roy. Soc. London, vol. B-211, pp. 151-180,1981.
  18. T. Lin, and J. L. Barron, "Image reconstruction error for optical flow," CVRP, 1992.
  19. D. J. Fleet, and A. D. Jepson, "Computation of component image velocity from local phase information," IJCV, vol. 5, no. 1, pp. 77-104, 1990. https://doi.org/10.1007/BF00056772