Skip to main content
SpringerLink
Log in

Motion estimation using the fast and adaptive bidimensional empirical mode decomposition

  • Special Issue
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Motion estimation is a basic step that can be used to serve several processes in computer vision. This motion is currently approximated by the visual displacement field called optical flow. Currently, several methods are used to estimate it, but a good compromise between computational cost and accuracy is hard to achieve. This paper tackles the problem by proposing a new technique based on the FABEMD (fast and adaptive bidimensional empirical mode decomposition) with the aim of improving the well-known pyramidal algorithm of Lucas and Kanade (LK) which, in principle, utilizes two consecutive frames extracted from video sequence to determine a dense optical flow. The proposed algorithm uses the FABEMD method to decompose each of the two considered frames into several BIMFs (bidimensional intrinsic mode functions) that are matched in number and proprieties. Thus, to compute the optical flow, the LK algorithm is applied to each of the two matching BIMFs which belong to the same mode of the decomposition. Although the implementation does not use an iterative refinement, the results show that the proposed approach is less sensitive to noise and provides improved motion estimation with a reduction of computing time compared to iterative methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Fleet, D.J., Jepson, A.D.: Computation of component image velocity from local phase information. Int. J. Comput. Vis. 5(1), 77–104 (1990)

    Article  Google Scholar 

  2. Argyriou, V., Vlachos, T.: A study of sub-pixel motion estimation using phase correlation. In: Centre for Vision, Speech and Signal Processing, University of Surrey (2006)

  3. Chalidabhongse, J., Kuo, C.: Fast motion vector estimation using multiresolution-spatio-temporal correlations. IEEE Trans. CSVT 7(3), 477–488 (1997)

    Google Scholar 

  4. Bruhn, A., Weickert, J., et al.: Variational optical flow computation in real time. In: IEEE Transactions on Image Processing, vol. 14(5), pp. 608–615 (2005)

  5. Anandan, P.: A computation framework and an algorithm for the measurement of visual motion. Int. J. Comput. Vis. 2, 219–232 (1989)

    Google Scholar 

  6. Horn, B., Schunck, B.: Determining optical flow. Artif. Intell. 17, 185–203 (1981)

    Article  Google Scholar 

  7. Lucas, B., Kanade, T.: An iterative image registration technique with an application in stereo vision. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)

  8. Stefano, M., Sergio, S.: Integrated video motion estimator with Retinex-like pre-processing for robust motion analysis in automotive scenarios: algorithmic and real-time architecture design. J. Real-Time Image Proc. 5(4), 275–289 (2010). doi:10.1007/s11554-009-0148-7

  9. Bhuiyan, S.M.A., Adhami, R.R., Khan, J.F.: Fast and adaptive bidimensional empirical mode decomposition using order-statistics filter based envelope estimation. EURASIP J. Adv. Signal Process. 2008, 1–18 (2008)

    Article  Google Scholar 

  10. Bhuiyan S.M.A., Adhami, R.R., Khan, J.F: A novel approach of fast and adaptive bidimensional empirical mode decomposition. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas, USA, pp. 1313–1316 (2008)

  11. Nunes, J.C., Bouaoune, Y., Delechelle, E., Niang, O., Bunel, P.H.: Image analysis by bidimensional empirical mode decomposition. Image Vis. Comput. 21, 1019–1026 (2003)

    Article  Google Scholar 

  12. Nunes, J.C., Guyot, S., Deléchelle, E.: Texture analysis based on local analysis of the bidimensional empirical mode decomposition. Mach. Vis. Appl. 16(3), 177–188 (2005)

    Article  Google Scholar 

  13. Barjatya, A.: Block matching algorithms for motion estimation. Technical Report, Utah State University (2004)

  14. Beauchemin, S.S., Barron, J.L.: The computation of optical flow. ACM Comput Surv 27(3), 433–467 (1995)

    Article  Google Scholar 

  15. Barron, J., Fleet, D., Beauchemin, S.: Performance of optical flow techniques. Int. J. Comput. Vis. 12(1), 43–77 (1994)

    Article  Google Scholar 

  16. Baker, S., Matthews, I.: Lucas-Kanade 20 years on: a unifying framework. Int. J. Comput. Vis. 46(3), 221–255 (2004)

    Article  Google Scholar 

  17. Wu, T.Y., Kanade, T., Cohn, J., Li, C.-C.: Optical flow estimation using wavelet motion model. In: Proceedings of the 6th International Conference on Computer Vision, pp. 992–998 (1998)

  18. Bouguet, J.-Y.: Pyramidal implementation of the Lucas Kanade feature tracker description of the algorithm. In: OpenCV Documents. Intel Corporation, Microprocessor Research Labs (1999)

  19. Kraus, M., Strengert, M.: Pyramid filters based on bilinear interpolation. In: Proceedings of the International Conference on Computer Graphics Theory and Applications, Barcelona, Spain, pp. 21–28 (2007)

  20. Bovik, A.: Handbook of Image and Video Processing, 2nd edn. Academic Press, London (2005)

  21. Linderhed, A.: 2D empirical mode decompositions in the spirit of image compression. Wavelet and independent component analysis applications IX. In: SPIE Proceedings, vol. 4738, pp. 1–8

  22. Simoncelli, E., Adelson, E., Heeger, D.: Probability distributions of optical flow. In: Proceedings of Conference on Computer Vision and Pattern Recognition, pp. 310–313 (1991)

Download references

Author information

Authors and Affiliations

  1. LIIAN, Department of Computer Science, Faculty of Science Dhar El Mahraz, BP 1796, Fez, Morocco

    M. A. Mahraz, J. Riffi & H. Tairi

  2. 18 Street Boufkrane Narjiss A, Fez, Morocco

    M. A. Mahraz

Authors
  1. M. A. Mahraz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Riffi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. Tairi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. A. Mahraz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mahraz, M.A., Riffi, J. & Tairi, H. Motion estimation using the fast and adaptive bidimensional empirical mode decomposition. J Real-Time Image Proc 9, 491–501 (2014). https://doi.org/10.1007/s11554-012-0259-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-012-0259-4

Keywords

Search

Navigation