On improving the robustness of differential optical flow

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 876-881

  Rashwan, Hatem A ^a   Puig, Domenec ^a   Garcia, Miguel Angel ^b  

^a UNIVERSITAT ROVIRA I VIRGILI   (Spain)

^b UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTED FLOWS; CONSECUTIVE IMAGES; GAUSSIAN FILTERING; GAUSSIAN FILTERS; GRADIENT IMAGES; LOSS OF ACCURACY; MODEL YIELDS; OPTICAL FLOW TECHNIQUES; PARTIAL DERIVATIVES; TENSOR VOTING;

FLOW FIELDS; IMAGE SEGMENTATION;

OPTICAL FLOWS;

EID: 84856650195     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCVW.2011.6130344     Document Type: Conference Paper

References (17)

1. B. D. Lucas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision", In Proc. DARPA Image Understanding Workshop, pp. 121-130, 1981.
2. B. K. P. Horn and B. G. Schunck, "Determining Optical Flow", Artifcial Intelligence, vol. 17, pp. 185-204, 1981.
3. J. Bïgun, G. H. Granlund and J. Wiklund, "Multidimensional orientation estimation with applications to texture analysis and optical flow", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13(8), pp 775-790, 1991.
4. J. Weickert and C. Schnörr, "Variational optic flow computation with a spatio-temporal smoothness constraint", Journal of Mathematical Imaging and Vision, vol. 14(3), pp. 245-255, 2001.
5. H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, H.-P. Seidel "Complementary optic flow", In Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, vol. 5681, pp. 207-220, 2009.
6. W. S. Tong, C. K. Tang, and G. Medioni, "First Order Tensor Voting, and Application to 3-D Scale Analysis", In Proc. Int. Conference on Computer Vision and Pattern Recognition, pp. 175-182, 2001.
7. C. K. Tang, G. Medioni and M. S. Lee, "Tensor Voting: theory and application", In Proc. Organization for Artificial Vision Systems, Kim Boyer and Sudeep Sarkar (Eds), Kluwer, 2000.
8. S. P. Liou and R. C. Jain, "Motion detection in spatiotemporal space", Journal of Computer Vision, Graphics, and Image Processing, vol. 45(2), pp. 227-250, 1989.
9. A. Bruhn, J. Weickert, T. Kohlberger and C. Schnörr, "A multigrid platform for real-time motion computation with discontinuity- preserving variational methods", International Journal of Computer Vision, vol. 70(3), pp. 257-277, 2006.
10. T. Brox, A. Bruhn, N. Papenberg and J. Weickert, "High accuracy optical flow estimation based on a theory for warping", In Proc. European Conference on Computer Vision, vol. 3024, Lecture Notes in Computer Science, pp. 25-36, 2004.
11. A. Bruhn, J. Weickert and C. Schnörr, "Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods", International Journal of Computer Vision, vol. 61(3), pp. 211-231, 2005.
12. Liu's website: people. csail. mit. edu/celiu/motion-Annotation.
13. Middlebury's website: vision. middlebury. edu/flow.
14. Define minimum SNR values. [Online]. http://www. wirelessnets. com/resources/tutorials/define-SNR-values. html.
15. S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black and R. Szeliski, "A database and evaluation methodology for optical flow", International Journal of Computer Vision, 92:1, pp. 1-31, 2011.
16. J. L. Barron, D. J. Fleet and S. S. Beauchemin. "Performance of Optical Flow Techniques", In Proc. In International Journal of Computer Vision, 12:1, pp. 43-77, 1994.
17. R. Moreno, M. A. Garcia, D. Puig, L. Pizarro, B. Burgeth and J. Weickert "On Improving the Efficiency of Tensor Voting", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 99, 2011.