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TV-L1 Optical Flow Estimation with Image Details Recovering Based on Modified Census Transform

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Book cover Advances in Visual Computing (ISVC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7431))

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Abstract

This paper proposes an improved optical flow estimation approach based on the total variational L1 minimization technique with weighted median filter. Furthermore, recovering image details using modified census transform algorithm improves the overall accuracy of estimating large scale displacements optical flow. On the other hand, the use of the Taylor expansion approximation in most of the optical flow approaches limits the ability to estimate movement of fast objects. Hence, a coarse-to-fine scheme is used to overcome such a problem of the cost of losing small details in the interpolation process where initial values are propagated from the coarse level to the fine one. The proposed algorithm improves the accuracy of the estimation process by integrating the correspondence results of the modified census transform into the coarse-to-fine module in order to recover the lost details. The outcome of the proposed approach yields state-of-the-art results on the Middlebury optical flow evaluations.

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References

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

    Article  Google Scholar 

  2. Lukas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Image Understanding Workshop (1981)

    Google Scholar 

  3. Bruhn, A., Weickert, J., Schnörr, C.: Lucas/kanade meets horn/schunck: Combining local and global optic flow methods. International Journal of Computer Vision 61, 211–231 (2005)

    Article  Google Scholar 

  4. 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)

    Google Scholar 

  5. Chambolle, A.: An algorithm for total variation minimization and applications. Journal of Mathematical Imaging and Vision 20, 89–97 (2004)

    Article  MathSciNet  Google Scholar 

  6. Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Computer Vision-ECCV 2004, pp. 25–36 (2004)

    Google Scholar 

  7. Sun, D., Roth, S., Black, M.: Secrets of optical flow estimation and their principles. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2432–2439. IEEE (2010)

    Google Scholar 

  8. Xu, L., Jia, J., Matsushita, Y.: Motion detail preserving optical flow estimation. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1293–1300. IEEE (2010)

    Google Scholar 

  9. Liu, C., Yuen, J., Torralba, A.: Sift flow: Dense correspondence across scenes and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 978–994 (2011)

    Article  Google Scholar 

  10. Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 500–513 (2011)

    Article  Google Scholar 

  11. Stein, F.: Efficient computation of optical flow using the census transform. Pattern Recognition, 79–86 (2004)

    Google Scholar 

  12. Puxbaum, P., Ambrosch, K.: Gradient-based modified census transform for optical flow. Advances in Visual Computing, 437–448 (2010)

    Google Scholar 

  13. Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Computer Visionâ ECCV 1994, pp. 151–158 (1994)

    Google Scholar 

  14. Froba, B., Ernst, A.: Face detection with the modified census transform. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 91–96. IEEE (2004)

    Google Scholar 

  15. Buades, A., Coll, B., Morel, J.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 60–65. IEEE (2005)

    Google Scholar 

  16. Gilboa, G., Osher, S.: Nonlocal operators with applications to image processing. Multiscale Model. Simul. 7, 1005–1028 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  17. Rashwan, H., Puig, D., Garcia, M.: On improving the robustness of differential optical flow. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 876–881. IEEE (2011)

    Google Scholar 

  18. Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M., Szeliski, R.: A database and evaluation methodology for optical flow. International Journal of Computer Vision 92, 1–31 (2011)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. GET Lab, University of Paderborn, 33098, Paderborn, Germany

    Mahmoud A. Mohamed & Baerbel Mertsching

Authors
  1. Mahmoud A. Mohamed
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  2. Baerbel Mertsching
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science, University of California at Irvine, 92697-3435, Irvine, CA

    Charless Fowlkes

  6. Eastman Kodak Company, 14650-2102, Rochester, NY, USA

    Sen Wang

  7. Department of Computer Science and Engineering, University of Colorado at Denver, 80217, Denver, CO, USA

    Min-Hyung Choi

  8. VRVis Zentrum für Virtual Reality and Visualisierung, 1220, Vienna, Austria

    Stephan Mantler

  9. California Institute for Telecommunications and Information Technology, University of California, 92093, San Diego, La Jolla, CA, USA

    Jürgen Schulze

  10. KAUST Visualizatioin Core Lab., 23955-6900, Thurwal, Saudi Arabia

    Daniel Acevedo

  11. Stony Brook University, 11794-4400, NY, USA

    Klaus Mueller

  12. Argonne National Laboratory, 60439, IL, USA

    Michael Papka

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© 2012 Springer-Verlag Berlin Heidelberg

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Mohamed, M.A., Mertsching, B. (2012). TV-L1 Optical Flow Estimation with Image Details Recovering Based on Modified Census Transform. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33179-4_46

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  • DOI: https://doi.org/10.1007/978-3-642-33179-4_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33178-7

  • Online ISBN: 978-3-642-33179-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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