International Conference on Advanced Concepts for Intelligent Vision Systems

Advanced Concepts for Intelligent Vision Systems pp 195-204 | Cite as

Cosine-Sine Modulated Filter Banks for Motion Estimation and Correction

  • Marco Maass
  • Huy Phan
  • Anita Möller
  • Alfred Mertins
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9386)

Abstract

We present a new motion estimation algorithm that uses cosine-sine modulated filter banks to form complex modulated filter banks. The motion estimation is based on phase differences between a template and the reference image. By using a non-downsampled version of the cosine-sine modulated filter bank, our algorithm is able to shift the template image over the reference image in the transform domain by only changing the phases of the template image based on a given motion field. We also show that we can correct small non-rigid motions by directly using the phase difference between the reference and the template images in the transform domain. We also include a first application in magnetic resonance imaging, where the Fourier space is corrupted by motion and we use the phase difference method to correct small motion. This indicates the magnitude invariance for small motions.

Keywords

Motion estimation Motion correction Motion invariance Cosine-sine modulated filter banks Motion mri 

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References

  1. 1.
    Austvoll, I.: A study of the yosemite sequence used as a test sequence for estimation of optical flow. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 659–668. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  2. 2.
    Bayram, I., Selesnick, I.W.: On the dual-tree complex wavelet packet and m -band transforms. IEEE Transactions on Signal Processing 56(6), 2298–2310 (2008)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chaux, C., Duval, L., Pesquet, J.C.: Image analysis using a dual-tree m-band wavelet transform. IEEE Transactions on Image Processing 15(8), 2397–2412 (2006)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Fleet, D.J., Jepson, A.D.: Computation of component image velocity from local phase information. International Journal of Computer Vision 5(1), 77–104 (1990)CrossRefGoogle Scholar
  5. 5.
    Goldstein, T., Osher, S.: The split bregman method for L1-regularized problems. SIAM Journal on Imaging Sciences 2(2), 323–343 (2009)MATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17(1–3), 185–203 (1981)CrossRefGoogle Scholar
  7. 7.
    Kyochi, S., Suzuki, T., Tanaka, Y.: A directional and shift-invariant transform based on m-channel rational-valued cosine-sine modulated filter banks. In: Proc. Asia-Pacific Signal Information Processing Association Annual Summit and Conference, Hollywood, California, pp. 1–4, December 2012Google Scholar
  8. 8.
    Kyochi, S., Uto, T., Ikehara, M.: Dual-tree complex wavelet transform arising from cosine-sine modulated filter banks. In: Proc. IEEE International Symposium on Circuits and Systems, Taipei, Taiwan, pp. 2189–2192, May 2009Google Scholar
  9. 9.
    Lu, J., Liou, M.L.: A simple and efficient search algorithm for block-matching motion estimation. IEEE Transactions on Circuits and Systems for Video Technology 7(2), 429–433 (1997)CrossRefGoogle Scholar
  10. 10.
    Maaß, M., Phan, H., Mertins, A.: Design of cosine-sine modulated filter banks without dc leakage. In: Proc. International Conference on Digital Signal Processing, Hong Kong, China, pp. 486–491, August 2014Google Scholar
  11. 11.
    Magarey, J., Kingsbury, N.G.: Motion estimation using a complex-valued wavelet transform. IEEE Transactions on Signal Processing 46(4), 1069–1084 (1998)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Möller, A., Maaß, M., Mertins, A.: Blind sparse motion MRI with linear subpixel interpolation. In: Handels, H., Deserno, T.M., Meinzer, H.P., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2015. Informatik aktuell, pp. 510–515. Springer, Heidelberg (2015)Google Scholar
  13. 13.
    Mota, C., Stuke, I., Aach, T., Barth, E.: Divide-and-conquer strategies for estimating multiple transparent motions. In: Jähne, B., Mester, R., Barth, E., Scharr, H. (eds.) IWCM 2004. LNCS, vol. 3417, pp. 66–77. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  14. 14.
    Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.G.: The dual-tree complex wavelet transform. IEEE Signal Processing Magazine 22(6), 123–151 (2005)CrossRefGoogle Scholar
  15. 15.
    Torr, P.H.S., Zisserman, A.: Feature based methods for structure and motion estimation. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) ICCV-WS 1999. LNCS, vol. 1883, pp. 278–294. Springer, Heidelberg (2000) CrossRefGoogle Scholar
  16. 16.
    Viholainen, A., Alhava, J., Renfors, M.: Implementation of parallel cosine and sine modulated filter banks for equalized transmultiplexer systems. In: Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 6, Salt Lake City, Utah, pp. 3625–3628, May 2001Google Scholar
  17. 17.
    Viholainen, A., Stitz, T.H., Alhava, J., Ihalainen, T., Renfors, M.: Complex modulated critically sampled filter banks based on cosine and sine modulation. In: Proc. IEEE International Symposium on Circuits and Systems, vol. 1, Scottsdale, Arizona, pp. I-833–I-836, May 2002Google Scholar
  18. 18.
    Yang, Z., Zhang, C., Xie, L.: Sparse MRI for motion correction. In: Proc. IEEE International Symposium on Biomedical Imaging, San Francisco, California, pp. 962–965, April 2013Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marco Maass
    • 1
  • Huy Phan
    • 1
  • Anita Möller
    • 1
  • Alfred Mertins
    • 1
  1. 1.Institute for Signal ProcessingUniversity of LübeckLübeckGermany

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