Journal of Mathematical Imaging and Vision

, Volume 18, Issue 1, pp 35–54 | Cite as

Fast Local and Global Projection-Based Methods for Affine Motion Estimation

  • Dirk Robinson
  • Peyman Milanfar


The demand for more effective compression, storage, and transmission of video data is ever increasing. To make the most effective use of bandwidth and memory, motion-compensated methods rely heavily on fast and accurate motion estimation from image sequences to compress not the full complement of frames, but rather a sequence of reference frames, along with “differences” between these frames which results from estimated frame-to-frame motion. Motivated by the need for fast and accurate motion estimation for compression, storage, and transmission of video as well as other applications of motion estimation, we present algorithms for estimating affine motion from video image sequences. Our methods utilize properties of the Radon transform to estimate image motion in a multiscale framework to achieve very accurate results. We develop statistical and computational models that motivate the use of such methods, and demonstrate that it is possible to improve the computational burden of motion estimation by more than an order of magnitude, while maintaining the degree of accuracy afforded by the more direct, and less efficient, 2-D methods.

motion estimation registration projection Radon transform multiscale affine performance complexity 


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  1. 1.
    A. Akutsu and Y. Tonomura, “Video tomography: An efficient method for camerawork extraction and motion analysis,” Transactions of the Institute of Electronics, Information, and Communications Engineers, Vol. J79D-II, No. 5, pp. 675–686, 1996.Google Scholar
  2. 2.
    S. Alliney and C. Morandi, “Digital image registration using projections,” IEEE Trans. Pattern Anal. Machine Intell., Vol. 8, No. 2, pp. 222–233, 1986.Google Scholar
  3. 3.
    J.L. Barron, D.J. Fleet, S.S. Beauchemin, and T.A. Burkitt, “Performance of optical flow techniques,” CVPR, Vol. 92, pp. 236–242, 1992.Google Scholar
  4. 4.
    J.R. Bergen, P. Anandan, K.J. Hanna, and R. Hingorani, “Hierachical model-based motion estimation,” in Proceedings European Conference on Computer Vision, 1992, pp. 237–252.Google Scholar
  5. 5.
    C. Bergeron and E. Dubois, “Gradient-based algorithms for block-oriented MAP estimation of motion and application to motion-compensated temporal interpolation,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 1, pp. 72–85, 1991.Google Scholar
  6. 6.
    M.J. Black and P. Anandan, “The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields,” Computer Vision and Image Understanding, Vol. 63, pp. 75–104, 1996.Google Scholar
  7. 7.
    L.G. Brown, “A survey of image registration techniques,” ACM Computing Surveys, Vol. 24, No. 4, pp. 325–376, 1992.Google Scholar
  8. 8.
    S.C. Cain, M.M. Hayat, and E.E. Armstrong, “Projection-based image registration in the presence of fixed-pattern noise,” IEEE Transactions on Image Processing, Vol. 10, No. 12, pp. 1860–1872, 2001.Google Scholar
  9. 9.
    F. Coudert, J. Benois-Pineau, and D. Barba, “Dominant motion estimation and video partitioning with a 1-D signal approach,” in SPIE Conference on Multimedia Storage and Archiving Systems III, Vol. 3527, 1998, pp. 283–294.Google Scholar
  10. 10.
    S. R. Deans, The Radon Transform and Some of its Applications, John Wiley and Sons: New York, 1983.Google Scholar
  11. 11.
    M.T. Heath, Scientific Computing: An Introductory Survey, McGraw-Hill: New York, 2002.Google Scholar
  12. 12.
    B.K.P. Horn, Robot Vision, MIT Press: Cambridge, 1986.Google Scholar
  13. 13.
    A. Jepson and M. Black, “Mixture models for optical flow computation,” in Proceedings Computer Vision and Pattern Recognition, June 1993, pp. 760–761.Google Scholar
  14. 14.
    S.M. Kay, Fundamentals of Signal Processing: Estimation Theory, Prentice Hall: Englewood Cliff, NJ, 1993.Google Scholar
  15. 15.
    Joon-Seek Kim and Rae-Hong Park, “A fast feature-based block matching algorithm using integral projections,” IEEE Journal on Selected Areas in Communications, Vol. 10, No. 5, pp. 968–971, 1992.Google Scholar
  16. 16.
    Hongche Liu, Tsai-Hong Hong, M. Herman, T. Camus, and R. Chellappa, “Accuracy vs. efficiency trade-offs in optical flow algorithms,” Computer Vision and Image Understanding, Vol. 72, pp. 271–286, 1998.Google Scholar
  17. 17.
    B.D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in DARPA81, 1981, pp. 121–130.Google Scholar
  18. 18.
    P. Milanfar, “Projection-based, frequency-domain estimation of superimposed translational motions,” Journal of the Optical Society of America, Vol. 13, No. 11, pp. 2151–2162, 1996.Google Scholar
  19. 19.
    P. Milanfar, “A model of the effect of image motion in the Radon transform domain,” IEEE Transactions on Image Processing, Vol. 8, No. 9, pp. 1276–1281, 1999.Google Scholar
  20. 20.
    S.A. Rajala, A.M. Riddle, and W.E. Snyder, “Application of the one-dimensional Fourier transform for tracking moving objects in noisy environments,” Computer Vision, Graphics, and Image Processing, Vol. 21, pp. 280–293, 1983.Google Scholar
  21. 21.
    D. Robinson and P. Milanfar, “Accuracy and efficiency tradeoffs in using projections for motion estimation,” in Proceedings of the 35th Asilomar Conference on Signals, Systems, and Computers, November 2001.Google Scholar
  22. 22.
    S.A. Seyedin, “Motion estimation using the Radon transform in dynamic scenes,” in Proceedings of the International Society for Optical Engineering, 1995, Vol. 2501, pp. 1337–1348.Google Scholar
  23. 23.
    C. Stiller and J. Konrad, “Estimating motion in image sequences,” IEEE Signal ProcessingMagazine,Vol. 16, pp. 70–91, 1999.Google Scholar
  24. 24.
    T. Tsuboi, A. Masubuchi, and S. Hirai, “Video-frame rate detection of position and orientation of planar motion objects using one-sided Radon transform,” in Proceedings IEEE Conference of Robotics and Automation, April 2001, Vol. 2, pp. 1233–1238.Google Scholar
  25. 25.
    Chengjie Tu, T.D. Tran, J.L. Prince, and P. Topiwala, “Projection-based block matching motion estimation,” in Proc. SPIE Applications of Digital Image Processing XXIII, August 2000, pp. 374–384.Google Scholar
  26. 26.
    Jiangsheng You, Weignou Lu, Jian Li, Gene Gindi, and Zhengrong Liang, “Image matching for translation, rotation, and uniform scaling by the Radon transform,” in Proceedings International Conference on Image Processing, 1998, Vol. 1, pp. 847–851.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Dirk Robinson
    • 1
  • Peyman Milanfar
    • 1
  1. 1.Department of Electrical EngineeringUniversity of California at Santa CruzSanta CruzUSA

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