Skip to main content
Springer Nature Link
Log in

A comparative study of new HOS-based estimators for moving objects in noisy video sequence

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The need for motion estimation (ME) arises quite often in many areas such as computer vision, target tracking, medical imaging, robotic vision. A five new estimators for frame-to-frame image ME are described in this paper. The new ME estimators exploit the higher-order statistics (HOS) characteristics of the received images, and various frequency weighting functions are used to prefilter the received images before calculating the generalized cross-cumulant function and, therefore, suppress the Gaussian noise effect. The estimators of interest are the HOS-ROTH impulse response, the HOS-phase transform, the HOS-smoothed coherence transform, the HOS-maximum likelihood and the HOS-Wiener estimators. Since the performances of the HOS-based estimators are considerably degraded by the signal-to-noise ratio level, this factor has been taken as a prime factor in benchmarking the different estimators. For robust ME it has been found that the HOS-Wiener estimator is particularly suited to this purpose. The accuracy of the estimators is also discussed.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Bovik, A.: The Essential Guide to Image Processing, 2nd edn, Chapter 3, p. 31. Academic Press Publishers, New York (2009)

  2. Estrela, V., Rivera, L.A., Bassani, M.H.S.: Pel-Recursive Motion Estimation Using the Expectation-Maximization Technique and Spatial Adaptation. WSCG (short papers), pp. 47–54 (2004)

  3. Sezan, M.I., Lagendijk, R.L.: Motion Analysis and Image Sequence Processing. Kluwer, Bostoa (1993)

    Book  Google Scholar 

  4. Namazi, N., Lee, C.: Nonuniform image motion estimation from noisy data. In: IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-38, pp. 364–366 (1990)

  5. Sayrol, E., Gasull, A., Fonollosa, J.R.: Motion estimation using higher order statistics. IEEE Trans. Image Process. 5(6), 1077–1084 (1996)

    Article  Google Scholar 

  6. Sayrol, E., gasull, T., Fonollosa, J.R.: Cost function for motion estimation based on higher order statistics. In: Proceedings of EU-SIPCO94, Edinburgh, UK, pp. 1117–1120 (1994)

  7. Anderson, J.M., Giannakis, G.B.: Noise Insensitive Image Estimation Algorithms Using Cumulants. In: Proceedings of ICASSP ’91, Toronto, pp. 2721–2724 (1991)

  8. Anderson, J.M., Giannakis, G.B.: Image motion estimation algorithms using cumulants. IEEE Trans. Image Process. 4, 346–357 (1995)

    Article  Google Scholar 

  9. Alaoui Ismaili, E.M., Ibn-Elhaj, E., Bouyakhf, E.H.: Noise-insensitive image optimal flow estimation using higher-order statistics. J. Opt. Soc. Am. A 26(5), 1212–1220 (2009)

    Article  Google Scholar 

  10. Marinescu, R.S., Buzo, A., Cucu, H., Burileanu, C.: Applying the accumulation of cross-power spectrum technique for traditional generalized cross-correlation time delay estimation. Int. J. Adv. Telecommun. 6(3–4), 98–108 (2013)

    Google Scholar 

  11. Ismaili Alaoui, E.M., Ibn-Elhaj, E.: A robust hierarchical motion estimation algorithm in noisy image sequences in the bispectrum domain. In: Signal, Image and Video Processing (SIViP). Springer, London (2008). doi:10.1007/s11760-008-0081-4

  12. Chandran, V., Boashash, B.: Carswell, Elgar, S.: Pattern recognition using invariants defined from higher order spectra: 2-D image inputs. IEEE Trans. Image Process. 6(5), 703–712 (1997)

    Article  Google Scholar 

  13. Cetin, A.E.: An iterative algorithm for signal reconstruction from Bispectrum. IEEE Trans. Signal Process. 39(12), 2621–2628 (1991)

    Article  MATH  Google Scholar 

  14. Heikkila, J.: Image scale and rotation from the phase-only bispectrum. In: Proceedings of IEEE Conference on Image Processing ICIP’04 (2004)

  15. Lii, K.S., Helland, K.N.: Cross bispectrum computation and variance estimation. ACM Trans. Math. Softw. 7, 284–294 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  16. Le Caillec, J.-M., Garello, R.: Comparison of statistical indices using 3rd-order statistics for nonlinearity detection. Signal Process. 84(3), 499–525 (2004)

Download references

Author information

Authors and Affiliations

  1. Computer Networks and Systems Laboratory, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco

    E. M. Ismaili Alaoui

  2. National Institute of Post and Telecommunications, Av. Allal Al Fassi, Madinat El Irfane, Rabat, Morocco

    E. Ibn-Elhaj

Authors
  1. E. M. Ismaili Alaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Ibn-Elhaj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. M. Ismaili Alaoui.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ismaili Alaoui, E.M., Ibn-Elhaj, E. A comparative study of new HOS-based estimators for moving objects in noisy video sequence. SIViP 11, 1297–1304 (2017). https://doi.org/10.1007/s11760-017-1098-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-017-1098-3

Keywords

Search

Navigation