Illumination-robust variational optical flow with photometric invariants

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4713 LNCS, Issue , 2007, Pages 152-162

  Mileva, Yana ^a   Bruhn, Andrés ^a   Weickert, Joachim ^a  

^a SAARLAND UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

INVARIANCE; PHOTOMETRY; PROBLEM SOLVING; ROBUSTNESS (CONTROL SYSTEMS);

CONSTANCY ASSUMPTION; PHOTOMETRIC INVARIANTS;

OPTICAL FLOWS;

EID: 38149027743     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74936-3_16     Document Type: Conference Paper

References (19)

1. Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 43-77 (1994)
2. Barron, J.L., Klette, R.: Quantitative colour optical flow. In: Proc. 16th International Conference on Pattern Recognition, Quebec City, Canada, August 2002, vol. 4, pp. 251-255. IEEE Computer Society Press, Los Alamitos (2002)
3. Black, M.J., Anandan, P.: The robust estimation of multiple motions: parametric and piecewise smooth flow fields. Computer Vision and Image Understanding 63(1), 75-104 (1996)
4. Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optic flow estimation based on a theory for warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25-36. Springer, Heidelberg (2004)
5. Bruhn, A., Weickert, J., Kohlberger, T., Schnörr, C.: A multigrid platform for real-time motion computation with discontinuity-preserving variational methods. International Journal of Computer Vision 70(3), 257-277 (2006)
6. Golland, P., Bruckstein, A.M.: Motion from color. Computer Vision and Image Understanding 68(3), 346-362 (1997)
7. Haußecker, H., Fleet, D.: Estimating optical flow with physical models of brightness variation. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 661-673 (2001)
8. Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence 17, 185-203 (1981)
9. Kim, Y-H., Martinez, A.M., Kak, A.C.: Robust motion estimation under varying illumination. Image and Vision Computing 23(1), 365-375 (2005)
10. Lee, H.C., Breneman, E.J., Schulte, C.P.: Modeling light reflection for computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 402-409 (1990)
11. Mémin, E., Pérez, P.: Hierarchical estimation and segmentation of dense motion fields. International Journal of Computer Vision 46(2), 129-155 (2002)
12. Nagel, H.-H.: Constraints for the estimation of displacement vector fields from image sequences. In: Proc. Eighth International Joint Conference on Artificial Intelligence, Karlsruhe, West Germany, August 1983, vol. 2, pp. 945-951 (1983)
13. Negahdaripour, S.: Revised definition of optical flow: integration of radiometric and geometric clues for dynamic scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(9), 961-979 (1998)
14. Ohta, N.: Optical flow detection by color images. In: Proc. Tenth International Conference on Pattern Recognition, Singapore, September 1989, pp. 801-805 (1989)
15. 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)
16. Shafer, S.A.: Using color to seperate reflection components. Color Research and Applications 10(4), 210-218 (1985)
17. Uras, S., Girosi, F., Verri, A., Torre, V.: A computational approach to motion perception. Biological Cybernetics 60, 79-87 (1988)
18. van de Weijer, J., Gevers, T.: Robust optical flow from photometric invariants. In: Proc. 2004 IEEE International Conference on Image Processing, Singapore, October 2004, vol. 3, pp. 1835-1838 (2004)
19. Weickert, J., Schnörr, C.: Variational optic flow computation with a spatio-temporal smoothness constraint. Journal of Mathematical Imaging and Vision 14(3), 245-255 (2001)