Skip to main content
SpringerLink
Log in

Optical flow estimation based on the structure–texture image decomposition

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

Abstract

Optical flow approaches for motion estimation calculate vector fields which determine the apparent velocities of objects in time-varying image sequences. Image motion estimation is a fundamental issue in low-level vision and is used in many applications in image sequence processing, such as robot navigation, object tracking, image coding and structure reconstruction. The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. Actually, several methods are used to estimate the optical flow, but a good compromise between computational cost and accuracy is hard to achieve. This work presents a combined local–global total variation approach with structure–texture image decomposition. The combination is used to control the propagation phenomena and to gain robustness against illumination changes, influence of noise on the results and sensitivity to outliers. The resulted method is able to compute larger displacements in a reasonable time.

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

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Nagel, H.-H.: Constraints for the estimation of displacement vector fields from image sequences. In: Proceedings of eighth international joint conference on artificial intelligence, vol 2, pp 945–951, Karlsruhe, West Germany, Aug (1983)

  3. Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings seventh international joint conference on artificial intelligence, pp 674–679, Vancouver, Canada, Aug (1981)

  4. Bigün, J., Granlund, G.H., Wiklund, J.: Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE Trans. Pattern Anal. Mach. Intell. 13(8), 775–790 (1991)

    Article  Google Scholar 

  5. Nagel, H.-H., Gehrke, A.: Spatiotemporally adaptive estimation and segmentation of oF-fields. In: H. Burkhardt and B. Neumann (eds), Computer Vision- ECCV ’98, volume 1407 of lecture notes in computer science, pp 86–102, Springer, Berlin, (1998)

  6. Tretiak, O., Pastor, L.: Velocity estimation from image sequences with second order differential operators. In: Proceedings seventh international conference on pattern recognition, pp 16–19, Montreal, Canada, July (1984)

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

    Article  Google Scholar 

  8. Galvin, B., McCane, B., Novins, K., Mason, D., Mills, S.: Recovering motion fields: an analysis of eight optical flow algorithms. In: Proceedings 1998 British machine vision conference, Southampton, England, Sept (1998)

  9. Bruhn, A., Weickert, J., Schnörr, C.: Lucas/Kanade meets Horn/Schunk: combining local and global optical flow methods. IJCV 61(3), 211–231 (2005)

    Article  Google Scholar 

  10. Drulea, M., Nedevschi, S.: Total variation regularization of local-global optical flow. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 318–323. IEEE (2011)

  11. Aujol, J., Gilboa, G., Chan, T., Osher, S.: Structure–texture image decomposition-modeling, algorithms, and parameter selection. Int. J. Comput. Vis. 67(1), 111–136 (2006)

    Article  MATH  Google Scholar 

  12. Aujol, F., Chambolle, A.: Dual norms and image decomposition models. Int. J. Comput. Vis. 63(1), 85–104 (2005)

    Article  MathSciNet  Google Scholar 

  13. Rudin, L., Osher, S., Fatimi, End E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)

  14. Cha, T.F., Osher, S., Shen, J.: The digital TV filter and nonlinear denoising. IEEE Trans. Image Process. 10, 231–241 (2001)

    Article  Google Scholar 

  15. Osher, S., Rudin, L.: Total variation based image restoration with free local constraints. In: Proceedings IEEE ICIP, vol I, Austin, TX, pp 31–35, (1994)

  16. Gilles, J.: Décomposition et détection de structures géométriques en imagerie. Doctoral Thesis, Ecole Normale Supérieure de Cachan, (2006)

  17. Meyer, Y.: Oscillating patterns in image processing and in some nonlinear evolution equations. The Fifteenth Dean Jacquelines B. Lewis Memorial Lectures, American Mathematical Society, (2001)

  18. Chambolle, A.: An algorithm for total variation minimization and application. J. Math. Imaging vis. 20(1–2), 89–97 (2004)

    MathSciNet  Google Scholar 

  19. Baker, S., et al.: A database and evaluation methodology for optical flow. Int. J. Comput. Vis. 92(1), 1–31 (2011)

    Article  Google Scholar 

  20. Taşdemir, K., Çetin, A.E.: Content-based video copy detection based on motion vectors estimated using a lower frame rate. Signal Image Video Process. 8(6), 1049–1057 (2014)

  21. Portelo, A., Figueiredo, M.A., Lemos, J.M., Marques, J.S.: Moving horizon estimation of pedestrian interactions using multiple velocity fields. Signal Image Video Process. 1–9 (2014). doi:10.1007/s11760-014-0620-0

  22. Perona, P., Malik, J.: Scale space and edge detection using anistropic diffusion, In: Proceedings IEEE computer society workshop on computer vision (1987),16–12

  23. Durand, Frédo, Dorsey, Julie: Fast bilateral filtering for the display of high-dynamic-range images, In: SIGGRAPH ’02: Proceedings of the 29th annual conference on computer graphics and interactive techniques, pp 257–266, New York, NY, USA ACM. (2002)

  24. Bournez, O.:’ Cours 5.2—Complexit’e parallèle’, LIX, Ecole Polytechnique

  25. Bao, L., Yang, Q., Jin, H.: Fast edge-preserving patchmatch for large displacement optical flow. In: CVPR’14 Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3534–3541. IEEE (2014)

  26. Mahraz, M.A., Riffi, J., Tairi, H.: Motion estimation using the fast and adaptive bidimensional empirical mode decomposition. J. Real-Time Image Process. 9(3), 491–501 (2014)

  27. Sun, D., Roth, S., Black, M.J.: A quantitative analysis of current practices in optical flow estimation and the principles behind them. Int. J. Comput. Vis. 106(2), 115–137 (2014)

  28. Lu, J., Yang, H., Min, D., Do, M.N.: Patch match filter: Efficient edge-aware filtering meets randomized search for fast correspondence field estimation. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1854–1861. IEEE (2013)

  29. Sun, D., Wulff, J., Sudderth, E.B., Pfister, H., Black, M.J.: A fully-connected layered model of foreground and background flow. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2451–2458. IEEE (2013)

  30. Mohamed, M., Rashwan, H., Mertsching, B., Garcia, M., Puig, D.: Illumination-robust optical flow approach using local directional pattern. Submitted to IEEE TCSVT (2013)

  31. Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimation and their principles, In: IEEE conference on computer vision and pattern recognition (CVPR). (2010)

Download references

Author information

Authors and Affiliations

  1. LIIAN, Department of Computer Science, Faculty of Science Dhar El Mahraz, University Sidi Mohamed Ben Abdellah, BP 1796, Fez, Morocco

    I. Bellamine & H. Tairi

Authors
  1. I. Bellamine
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. Tairi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. Bellamine.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bellamine, I., Tairi, H. Optical flow estimation based on the structure–texture image decomposition. SIViP 9 (Suppl 1), 193–201 (2015). https://doi.org/10.1007/s11760-015-0772-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-015-0772-6

Keywords

Search

Navigation