Illumination-robust variational optical flow using cross-correlation

Computer Vision and Image Understanding

Volumn 114, Issue 10, 2010, Pages 1104-1114

  Molnár, József ^a   Chetverikov, Dmitry ^b   Fazekas, Sándor ^b  

^a EÖTVÖS LORÁND UNIVERSITY   (Hungary)

^b INSTITUTE FOR COMPUTER SCIENCE AND CONTROL   (Hungary)

Author keywords

Cross correlation; Illumination robustness; Variational optical flow

Indexed keywords

CROSS CORRELATIONS; DICHROMATIC REFLECTION MODEL; ENERGY FUNCTIONALS; FAST OPERATION; GREY-LEVEL; GREYSCALE; ILLUMINATION CHANGES; ILLUMINATION-ROBUSTNESS; IMAGE COMPONENTS; NUMBER OF STATE; NUMERICAL SCHEME; PHOTOMETRIC INVARIANTS; SYNTHETIC DATA; VARIATIONAL METHODS; VARIATIONAL OPTICAL FLOW; VIDEO PROCESSING APPLICATIONS;

COLOR; OPTICAL FLOWS;

OPTICAL CORRELATION;

EID: 77956178170     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2010.07.006     Document Type: Article

References (32)

1. Y. Mileva, A. Bruhn, and J. Weickert Illumination-robust variational optical flow with photometric invariants Lecture Notes in Computer Science vol. 4713 2007 Springer 152 162
2. S. Shafer Using color to separate reflection components Color Research and Applications 10 4 1985 210 218
3. J. Gibson The Senses Considered as Perceptual Systems 1966 Houghton Mifflin Boston
4. V. Bruce, P.R. Green, and M. Georgeson Visual Perception 1996 Psychology Press UK
5. B.K.P. Horn Robot Vision 1986 McGraw-Hill New York
6. D. Todorovic A gem from the past: Pleikart Stumpf's anticipation of the aperture problem, Reichardt detectors, and perceived motion loss at equiluminance Perception 25 1996 1235 1242
7. J.L. Barron, D.J. Fleet, and S.S. Beauchemin Performance of optical flow techniques International Journal of Computer Vision 12 1 1994 43 77
8. B.K.P. Horn, and B.G. Schunck Determining optical flow Artificial Intelligence 17 1981 185 203
9. B.D. Lucas, T. Kanade, An iterative image registration technique with an application to stereo vision, in: DARPA Image Understanding Workshop, 1981, pp. 121-130.
10. M. Bertozzi, A. Broggiand, and A. Fasciol Vision-based intelligent vehicles: state of the art and perspectives Robotics and Autonomous Systems 31 2000 1 16
11. V. Kastrinaki, M. Zervakis, and K. Kalaitzakis A survey of video processing techniques for traffic applications Image and Vision Computing 21 2003 359 381
12. S. Negahdaripour Revised definition of optical flow: integration of radiometric and geometric cues for dynamic scene analysis IEEE Transactions on Pattern Analysis and Machine Intelligence 20 1996 961 979
13. Y.-H. Kim, A. Martinez, and A. Kak Robust motion estimation under varying illumination Image and Vision Computing 23 2005 365 375
14. T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, in: Proceedings of the European Conference on Computer Vision, vol. IV, 2004, pp. 25-36.
15. C. Zach, T. Pock, H. Bischof, A duality based approach for realtime TV-L1 optical flow, in: Proceedings of the DAGM, 2007, pp. 214-223.
16. G. Hermosillo, C. Chefd'hotel, and O. Faugeras Variational methods for multimodal image matching International Journal of Computer Vision 50 2002 329 343
17. L. Alvarez, J. Weickert, and J. Sanchez Reliable estimation of dense optical flow fields with large displacements International Journal of Computer Vision 39 2000 41 56
18. G.H. Valadez, Method and system for motion compensation in a temporal sequence of images, US Patent Application Publication, Pub. No.: US 2005/0265611 A1, 2005.
19. J.-P. Pons, R. Keriven, O. Faugeras, G. Hermosillo, Variational stereovision and 3D scene flow estimation with statistical similarity measures, in: Proceedings of the International Conference on Computer Vision, vol. 1, 2003, pp. 597-602.
20. S. Fazekas, D. Chetverikov, J. Molnar, An implicit non-linear numerical scheme for illumination-robust variational optical flow, in: Proceedings of the British Machine Vision Conference, 2009. URL .
21. J. Molnar, D. Chetverikov, Illumination-robust variational optical flow based on cross-correlation, in: Proceedings of the 33rd Workshop of the Austrian Association for Pattern Recognition, 2009, pp. 119-128.
22. J. Weickert, A. Bruhn, T. Brox, N. Papenberg, Mathematical models for registration and applications to medical imaging, in: Mathematics in Industry, vol. 10, Springer, Berlin, Heidelberg, 2006, pp. 103-136 (Chapter: A survey on variational optic flow methods for small displacements).
23. N. Papenberg, A. Bruhn, T. Brox, S. Didas, and J. Weickert Highly accurate optic flow computation with theoretically justified warping International Journal of Computer Vision 67 2006 141 158
24. Y. Mileva, A. Bruhn, J. Weickert, Illumination-robust variational optical flow with photometric invariants, in: DAGM-Symposium, 2007, pp. 152-162.
25. T. Amiaz, and N. Kiryati Piecewise-smooth dense optical flow via level sets International Journal of Computer Vision 68 2 2006 111 124
26. T. Amiaz, E. Lubetzky, and N. Kiryati Coarse to over-fine optical flow estimation Pattern Recognition 60 2007 2496 2503
27. W. Press, S. Teukolsky, W. Vetterling, and B. Flannery Numerical Recipes in C second ed. 1992 Cambridge University Press Cambridge, UK
28. S. Baker, S. Roth, D. Scharstein, M. Black, J. Lewis, R. Szeliski, A database and evaluation methodology for optical flow, in: Proceedings of the International Conference on Computer Vision, 2007, pp. 1-8.
29. B. McCane, K. Novins, D. Crannitch, and B. Galvin On benchmarking optical flow Computer Vision and Image Understanding 84 2001 126 142
30. University of Karlsruhe, Institute of Algorithms and Cognitive Systems, Image Sequence Server, 1998. i21www.ira.uka.de/image-sequences/.
31. A. Bruhn, J. Weickert, and C. Schnörr Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods International Journal of Computer Vision 61 3 2005 211 231
32. E. Mémin, and P. Pérez Hierarchical estimation and segmentation of dense motion fields International Journal of Computer Vision 46 2002 129 155