Skip to main content

A Comparison of Isotropic and Anisotropic Second Order Regularisers for Optical Flow

  • Conference paper
  • First Online:
Scale Space and Variational Methods in Computer Vision (SSVM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10302))

Abstract

In variational optical flow estimation, second order regularisation plays an important role, since it offers advantages in the context of non-fronto-parallel motion. However, in contrast to first order smoothness constraints, most second order regularisers are limited to isotropic concepts. Moreover, the few existing anisotropic concepts are lacking a comparison so far. Hence, our contribution is twofold. (i) First, we juxtapose general concepts for isotropic and anisotropic second order regularization based on direct second order methods, infimal convolution techniques, and indirect coupling models. For all the aforementioned strategies suitable optical flow regularisers are derived. (ii) Second, we show that modelling anisotropic second order smoothness terms gives an additional degree of freedom when penalising deviations from smoothness. This in turn allows us to propose a novel anisotropic strategy which we call double anisotropic regularisation. Experiments on the two KITTI benchmarks show the qualitative differences between the different strategies. Moreover, they demonstrate that the novel concept of double anisotropic regularisation is able to produce excellent results.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Braux-Zin, J., Dupont, R., Bartoli, A.: A general dense image matching framework combining direct and feature-based costs. In: Proceedings of International Conference on Computer Vision, pp. 185–192 (2013)

    Google Scholar 

  2. Bredies, K., Kunisch, K., Pock, T.: Total generalized variation. SIAM J. Imaging Sci. 3(3), 492–526 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  3. Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24673-2_3

    Chapter  Google Scholar 

  4. Chambolle, A., Lions, P.-L.: Image recovery via total variation minimization and related problems. Numer. Math. 76(2), 167–188 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chan, T., Marquina, A., Mulet, P.: High-order total variation-based image restoration. SIAM J. Sci. Comput. 22(2), 503–516 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  6. Charbonnier, P., Blanc-Féraud, L., Aubert, G., Barlaud, M.: Deterministic edge-preserving regularization in computed imaging. IEEE Trans. Image Process. 6(2), 298–311 (1997)

    Article  Google Scholar 

  7. Demetz, O., Stoll, M., Volz, S., Weickert, J., Bruhn, A.: Learning brightness transfer functions for the joint recovery of illumination changes and optical flow. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 455–471. Springer, Cham (2014). doi:10.1007/978-3-319-10590-1_30

    Google Scholar 

  8. Ferstl, D., Reinbacher, C., Ranftl, R., Rüther, M., Bischof, H.: Image guided depth upsampling using anisotropic total generalized variation. In: Proceedings of International Conference on Computer Vision, pp. 993–1000 (2013)

    Google Scholar 

  9. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361 (2012)

    Google Scholar 

  10. Hafner, D., Schroers, C., Weickert, J.: Introducing maximal anisotropy into second order coupling models. In: Gall, J., Gehler, P., Leibe, B. (eds.) GCPR 2015. LNCS, vol. 9358, pp. 79–90. Springer, Cham (2015). doi:10.1007/978-3-319-24947-6_7

    Chapter  Google Scholar 

  11. Hewer, A., Weickert, J., Scheffer, T., Seibert, H., Diebels, S.: Lagrangian strain tensor computation with higher order variational models. In: Proceedings of British Machine Vision Conference, pp. 129.1–129.10 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  13. Lefkimmiatis, S., Ward, J.P., Unser, M.: Hessian Schatten-norm regularization for linear inverse problems. IEEE Trans. Image Process. 22(5), 1873–1888 (2013)

    Article  MathSciNet  Google Scholar 

  14. Lellmann, J., Morel, J.-M., Schönlieb, C.-B.: Anisotropic third-order regularization for sparse digital elevation models. In: Kuijper, A., Bredies, K., Pock, T., Bischof, H. (eds.) SSVM 2013. LNCS, vol. 7893, pp. 161–173. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38267-3_14

    Chapter  Google Scholar 

  15. Lenzen, F., Becker, F., Lellmann, J.: Adaptive second-order total variation: an approach aware of slope discontinuities. In: Kuijper, A., Bredies, K., Pock, T., Bischof, H. (eds.) SSVM 2013. LNCS, vol. 7893, pp. 61–73. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38267-3_6

    Chapter  Google Scholar 

  16. Lysaker, M., Lundervold, A., Tai, X.C.: Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Trans. Image Process. 12(12), 1579–1590 (2003)

    Article  MATH  Google Scholar 

  17. Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3061–3070 (2015)

    Google Scholar 

  18. Nagel, H.-H., Enkelmann, W.: An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Trans. Patt. Anal. Mach. Intell. 8(5), 565–593 (1986)

    Article  Google Scholar 

  19. Nir, T., Bruckstein, A.M., Kimmel, R.: Over-parameterized variational optical flow. Int. J. Comput. Vis. 76(2), 205–216 (2008)

    Article  Google Scholar 

  20. Perona, P., Malik, J.: Scale space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 2, 629–639 (1990)

    Article  Google Scholar 

  21. Ranftl, R., Gehrig, S., Pock, T., Bischof, H.: Pushing the limits of stereo using variational stereo estimation. In: IEEE Intelligent Vehicles Symposium, pp. 401–407 (2012)

    Google Scholar 

  22. Ranftl, R., Bredies, K., Pock, T.: Non-local total generalized variation for optical flow estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 439–454. Springer, Cham (2014). doi:10.1007/978-3-319-10590-1_29

    Google Scholar 

  23. Ranftl, R.: Higher-Order Variational Methods for Dense Correspondence Problems. Ph.D thesis. Graz University of Technology, Austria (2014)

    Google Scholar 

  24. Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: EpicFlow: Edge-preserving interpolation of correspondences for optical flow. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1164–1172 (2015)

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  26. Scherzer, O.: Denoising with higher order derivatives of bounded variation and an application to parameter estimation. Computing 60(1), 1–27 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  27. Sun, D., Roth, S., Lewis, J.P., Black, M.J.: Learning optical flow. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 83–97. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88690-7_7

    Chapter  Google Scholar 

  28. Trobin, W., Pock, T., Cremers, D., Bischof, H.: An Unbiased second-order prior for high-accuracy motion estimation. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 396–405. Springer, Heidelberg (2008). doi:10.1007/978-3-540-69321-5_40

    Chapter  Google Scholar 

  29. Vogel, O., Bruhn, A., Weickert, J., Didas, S.: Direct shape-from-shading with adaptive higher order regularisation. In: Sgallari, F., Murli, A., Paragios, N. (eds.) SSVM 2007. LNCS, vol. 4485, pp. 871–882. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72823-8_75

    Chapter  Google Scholar 

  30. Weickert, J., Welk, M., Wickert, M.: L 2-Stable nonstandard finite differences for anisotropic diffusion. In: Kuijper, A., Bredies, K., Pock, T., Bischof, H. (eds.) SSVM 2013. LNCS, vol. 7893, pp. 380–391. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38267-3_32

    Chapter  Google Scholar 

  31. Weickert, J., Schnörr, C.: A theoretical framework for convex regularizers in PDE-based computation of image motion. Int. J. Comput. Vision 45(3), 245–264 (2001)

    Article  MATH  Google Scholar 

  32. Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., Bischof, H.: Anisotropic Huber-\(L^1\) optic flow. In: Proceedings of British Machine Vision Conference (2009)

    Google Scholar 

  33. Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-\(L^1\) optical flow. In: Proceedings of German Pattern Recognition, pp. 214–223 (2007)

    Google Scholar 

  34. Zimmer, H., Bruhn, A., Weickert, J.: Optic flow in harmony. Int. J. Comput. Vis. 93(3), 368–388 (2011)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

We thank the German Research Foundation (DFG) for financial support within project B04 of SFB/Transregio 161.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daniel Maurer .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Maurer, D., Stoll, M., Volz, S., Gairing, P., Bruhn, A. (2017). A Comparison of Isotropic and Anisotropic Second Order Regularisers for Optical Flow. In: Lauze, F., Dong, Y., Dahl, A. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science(), vol 10302. Springer, Cham. https://doi.org/10.1007/978-3-319-58771-4_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-58771-4_43

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58770-7

  • Online ISBN: 978-3-319-58771-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics