Robust brightness description for computing optical flow

BMVC 2008 - Proceedings of the British Machine Vision Conference 2008

Volumn , Issue , 2008, Pages

  Kharbat, M ^a   Aouf, N ^a   Tsourdos, A ^a   White, B ^a  

^a CRANFIELD UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; LUMINANCE; OPTICAL FLOWS; REGRESSION ANALYSIS;

BRIGHTNESS CONSTANCIES; GEOMETRIC MOMENT; INTENSITY FUNCTIONS; OPTICAL FLOW ALGORITHM; REGRESSION METHOD; SPECULAR REFLECTIONS; SYNTHETIC AND REAL DATA; VISUAL PHENOMENA;

PIXELS;

EID: 84898481946     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C.22.46     Document Type: Conference Paper

References (20)

1. Simon Baker, Stefan Roth, Daniel Scharstein, Michael J. Black, J.P. Lewis, and Richard Szeliski. A database and evaluation methodology for optical flow. Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pages 1-8, 14-21 Oct. 2007.
2. J.L. Barron, D.J. Fleet, and S.S. Beauchemin. Performance of optical flow techniques. International Journal of Computer Vision, 12(1):43-77, 1994.
3. J.R. Bergen, P. Anandan, K.J. Hanna, and R. Hingorani. Hierarchical model-based motion estimation. ECCV, 92:237-252.
4. M.J. Black, D.J. Fleet, and Y. Yacoob. Robustly estimating changes in image appearance. 78(1):8-31, April 2000.
5. Jan Flusser, Barbara Zitova, and Timas Suk. Moments and moment invariant in image analysis. Technical report, Accessed online on the 25th of Feb. 2008 http://staff.utia.cas.cz/zitova/tutorial/icip07.html, 2007.
6. S. Ghosal and R. Mehrotra. Robust optical-flow estimation using semi-invariant local features. 30(2):229-237, February 1997.
7. H.W. Haussecker and D.J. Fleet. Computing optical flow with physical models of brightness variation. 23(6):661-673, June 2001.
8. Berthold K. P. Horn and Brian G. Schunck. Determining optical flow. In Shape recovery, pages 389-407. Jones and Bartlett Publishers, Inc., 1992. 136-790.
9. Ming-Kuei Hu. Visual pattern recognition by moment invariants. Information Theory, IEEE Transactions on, 8(2):179-187, 1962.
10. T.M. Hupkens and J. de Clippeleir. Noise and intensity invariant moments. 16:371-376, 1995.
11. Yeon-Ho Kim, Aleix M. Martinez, and Avi C. Kak. Robust motion estimation under varying illumination. Image and Vision Computing, 23(4):365-375, 2005.
12. B.D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In IJCAI81, pages 674-679, 1981.
13. B. McCane, K. Novins, D. Crannitch, and B. Galvin. On benchmarking optical flow. Computer Vision and Image Understanding, 84:126-143, 2001. doi:10.1006/cviu.2001.0930.
14. R Mukundan and K R Ramakrishnan. Moment Functions in Image Analysis: Theory and Application. World Scientific, 1998.
15. C. Ruberto and A. Morgera. A comparison of 2-d moment-based description techniques. pages 212-219, 2005.
16. H. Spies, B. Jahne, and J.L. Barron. Range flow estimation. 85(3):209-231, March 2002.
17. Emanuele Trucco and Alessandro Verri. Introductory techniques for 3-D computer vision. Prentice Hall, 1998.
18. L.J. Van Gool, T. Moons, and D. Ungureanu. Affine/photometric invariants for planar intensity patterns. pages I:642-651, 1996.
19. M. Ye and R.M. Haralick. Two-stage robust optical flow estimation. pages II: 623-628, 2000.
20. Qiang Zhou, D. Chelberg, Yujing Zeng, and J. Starzyk. Robust optical flow estimation using invariant feature. Proceedings of the 33rd Southeastern Symposium on System Theory, pages 263-267, Mar 2001.