Advertisement

An Adaptive Confidence Measure for Optical Flows Based on Linear Subspace Projections

  • Claudia Kondermann
  • Daniel Kondermann
  • Bernd Jähne
  • Christoph Garbe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4713)

Abstract

Confidence measures are important for the validation of optical flow fields by estimating the correctness of each displacement vector. There are several frequently used confidence measures, which have been found of at best intermediate quality. Hence, we propose a new confidence measure based on linear subspace projections. The results are compared to the best previously proposed confidence measures with respect to an optimal confidence. Using the proposed measure we are able to improve previous results by up to 31%.

Keywords

Ground Truth Displacement Vector Optical Flow Angular Error Confidence Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kondermann, C., Kondermann, D., Jähne, B., Garbe, C.: Comparison of Confidence and Situation Measures and their Optimality for Optical Flows. International Journal of Computer Vision (submitted, 2007)Google Scholar
  2. 2.
    Anandan, P.: A computational framework and an algorithm for the measurement of visual motion. Internat. Journal of Computer Vision 2, 283–319 (1989)CrossRefGoogle Scholar
  3. 3.
    Barron, J., Fleet, D., Beauchemin, S.: Performance of Optical Flow Techniques. International Journal of Computer Vision 12(1), 43–77 (1994)CrossRefGoogle Scholar
  4. 4.
    Haußecker, H., Spies, H.: Motion. In: Handbook of Computer Vision and Applications. ch. 13, vol. 2, Academic Press, London (1999)Google Scholar
  5. 5.
    Papenberg, N., Bruhn, A., Brox, T., Didas, S., Weickert, J.: Highly Accurate Optic Flow Computation with Theoretically Justified Warping. International Journal of Computer Vision 67(2), 141–158 (2006)CrossRefGoogle Scholar
  6. 6.
    Weickert, J., Schnörr, C.: A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion. International Journal of Computer Vision 45(3), 245–264 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Lucas, B., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision (DARPA). In: Proceedings of the 1981 DARPA Image Understanding Workshop, pp. 121–130 (1981)Google Scholar
  8. 8.
    Horn, B., Schunk, B.: Determining Optical Flow. Artificial Intelligence 17, 185–204 (1981)CrossRefGoogle Scholar
  9. 9.
    Bruhn, A., Weickert, J., Schnörr, C.: Lucas/Kanade meets Horn/Schunck: Combining Local and Global Optic Flow Methods. International Journal of Computer Vision 61(3), 211–231 (2005)CrossRefGoogle Scholar
  10. 10.
    Bruhn, A., Weickert, J.: A Confidence Measure for Variational Optic flow Methods. Springer Netherlands, pp. 283–298 (2006)Google Scholar
  11. 11.
    Bigün, J., Granlund, G.H., Wiklund, J.: Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE journal of pattern analysis and machine intelligence (PAMI) 13(8), 775–790 (1991)CrossRefGoogle Scholar
  12. 12.
    Barth, E.: The minors of the structure tensor. In: Proceedings of the DAGM (2000)Google Scholar
  13. 13.
    Mota, C., Stuke, I., Barth, E.: Analytical Solutions For Multiple Motions. In: Proceedings of the International Conference on Image Processing ICIP (2001)Google Scholar
  14. 14.
    McCane, B., Novins, K., Crannitch, D., Galvin, B.: On Benchmarking Optical Flow. Computer Vision and Image Understanding 84(1), 126–143 (2001), http://www.cs.otago.ac.nz/research/vision/Research/OpticalFlow/opticalflow.html zbMATHCrossRefGoogle Scholar
  15. 15.
    Scharr, H.: Optimal filters for extended optical flow. In: Jähne, B., Mester, R., Barth, E., Scharr, H. (eds.) IWCM 2004. LNCS, vol. 3417, Springer, Heidelberg (2007)Google Scholar
  16. 16.
    Roth, S., Black, M.: On the spatial statistics of optical flow. In: Tenth IEEE International Conference on Computer Vision, vol. 1, pp. 42–49. IEEE, Los Alamitos (2005)CrossRefGoogle Scholar
  17. 17.
    Black, M., Yacoob, Y., Jepson, A., Fleet, D.: Learning Parameterized Models of Image Motion. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR) (1997)Google Scholar
  18. 18.
    Nieuwenhuis, C., Yan, M.: Knowledge Based Image Enhancement Using Neural Networks. In: Proceedings of the 18th International Conference on Pattern Recognition, pp. 814–817 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Claudia Kondermann
    • 1
  • Daniel Kondermann
    • 1
  • Bernd Jähne
    • 1
  • Christoph Garbe
    • 1
  1. 1.Interdisciplinary Center for Scientific Computing, University of HeidelbergGermany

Personalised recommendations