An experimental study of image components and data metrics for illumination-robust variational optical flow

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2010, Pages 1694-1697

  Chetverikov, Dmitry ^a   Molnár, József ^b  

^a INSTITUTE FOR COMPUTER SCIENCE AND CONTROL   (Hungary)

^b EÖTVÖS LORÁND UNIVERSITY   (Hungary)

Author keywords

[No Author keywords available]

Indexed keywords

CROSS CORRELATIONS; ENERGY FUNCTIONALS; EXPERIMENTAL STUDIES; ILLUMINATION CHANGES; IMAGE COMPONENTS; INTELLIGENT VEHICLES; MACHINE VISION; OPTICAL FLOW ALGORITHM; TEST DATA; TRAFFIC MONITORING; VISION BASED;

COMPUTER VISION; INTELLIGENT VEHICLE HIGHWAY SYSTEMS;

OPTICAL FLOWS;

EID: 78149492366     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2010.419     Document Type: Conference Paper

References (13)

1. S. Baker, S. Roth, D. Scharstein, M. Black, J. Lewis, and R. Szeliski. A database and evaluation methodology for optical flow. In Proc. International Conf. on Computer Vision, pages 1-8, 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, February 1994.
3. T. Brox, A. Bruhn, N. Papenberg, and J.Weickert. High accuracy optical flow estimation based on a theory for warping. In Proc. European Conf. on Computer Vision, volume IV, pages 25-36, 2004.
4. S. Fazekas, D. Chetverikov, and J. Molnar. An implicit non-linear numerical scheme for illuminationrobust variational optical flow. In Proc. British Machine Vision Conf., 2009.
5. B. K. P. Horn and B. G. Schunck. Determining optical flow. Artificial Intelligence, 17:185-203, 1981.
6. Y.-H. Kim, A. Martinez, and A. Kak. Robust motion estimation under varying illumination. Image and Vision Computing, 23:365-375, 2005. (Pubitemid 40206219)
7. B. McCane, K. Novins, D. Crannitch, and B. Galvin. On Benchmarking Optical Flow. Computer Vision and Image Understanding, 84:126-142, 2001.
8. Y. Mileva, A. Bruhn, and J. Weickert. Illumination-Robust Variational Optical Flow with Photometric Invariants. In Lecture Notes in Computer Science, volume 4713, pages 152-162. Springer, 2007.
9. Y. Mileva, A. Bruhn, and J. Weickert. Illumination-robust variational optical flow with photometric invariants. In DAGM-Symposium, pages 152-162, 2007.
10. J. Molnar and D. Chetverikov. Illumination-robust variational optical flow based on cross-correlation. In Proc. 33rd Workshop of the Austrian Association for Pattern Recognition, pages 119-128, 2009.
11. S. Negahdaripour. Revised definition of optical flow: integration of radiometric and geometric cues for dynamic scene analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, 20:961-979, 1996.
12. S. Shafer. Using color to separate reflection components. Color Research and Applications, 10(4):210-218, 1985.
13. C. Zach, T. Pock, and H. Bischof. A Duality Based Approach for Realtime TV-L1 Optical Flow. In Proc. DAGM, pages 214-223, 2007.