Symmetrical dense optical flow estimation with occlusions detection

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

Volumn 2350, Issue , 2002, Pages 721-735

  Alvarez, Luis ^a   Deriche, Rachid ^a   Papadopoulo, Théo ^a   Sánchez, Javier ^a  

^a UNIVERSITY OF LAS PALMAS DE GRAN CANARIA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; EQUATIONS OF MOTION; FLOW FIELDS;

DENSE OPTICAL FLOW; ENERGY FUNCTIONALS; LAGRANGE EQUATION; MULTI-RESOLUTION TECHNIQUES; REAL IMAGES; TRADITIONAL TECHNIQUES; VARIATIONAL APPROACHES; VARIATIONAL PROBLEMS;

OPTICAL FLOWS;

EID: 84944406615     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47969-4_48     Document Type: Conference Paper

References (31)

1. L. Alvarez, R. Deriche, J. Weickert, and J. Sànchez. Dense disparity map estimation respecting image discontinuities: A PDE and scale-space based approach. In IAPR International Workshop on Machine Vision Applications, Tokyo, Japan, Nov. 2000. The University of Tokyo. A revised version has been accepted for publication in 2002 in Journal of Visual Communication and Image Representation.
2. L. Alvarez, P. Lions, and J. Morel. Image selective smoothing and edge detection by nonlinear diffusion (II). SIAM Journal of Numerical Analysis, 29:845–866, 1992.
3. L. Alvarez, J. Weickert, and J. Sànchez. A scale-space approach to nonlocal optical flowcalculations. In M. Nielsen, P. Johansen, O. Olsen, and J. Weickert, editors, Scale-Space Theories in Computer Vision, volume 1682 of Lecture Notes in Computer Science, pages 235–246. Springer–Verlag, 1999.
4. L. Alvarez, J. Weickert, and J. Sànchez. Reliable estimation of dense optical flow fields with large displacements. The International Journal of Computer Vision, 39(1):41–56, Aug. 2000.
5. G. Aubert, R. Deriche, and P. Kornprobst. Computing optical flowvia variational techniques. SIAM Journal of Applied Mathematics, 60(1):156–182, 1999.
6. J. Barron, D. Fleet, and S. Beauchemin. Performance of optical flowtec hniques. The International Journal of Computer Vision, 12(1):43–77, 1994.
7. M. Black and P. Rangarajan. On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. The International Journal of Computer Vision, 19(1):57–91, 1996.
8. V. Caselles, J. Morel, G. Sapiro, and A. Tannenbaum. Introduction to the special issue on partial differential equations and geometry-driven diffusion in image processing and analysis. IEEE Transactions on Image Processing, 7(3):269–273, 1998.
9. G. E. Christensen. Consistent linear-elastic transformations for image matching. In A. K. et al, editor, Proceedings of IPMI, volume 1613 of LNCS, pages 224–237, Berlin, Heildelberg, 1999. Springer-Verlag.
10. I. Cohen. Nonlinear variational method for optical flowcomputation. In Scandinavian Conference on Image Analysis, volume 1, pages 523–530, 1993.
11. R. Deriche, P. Kornprobst, and G. Aubert. Optical flowestimation while preserving its discontinuities: A variational approach. In Proceedings of the 2nd Asian Conference on Computer Vision, volume 2, pages 71–80, Singapore, Dec. 1995.
12. W. Enkelmann. Investigation of multigrid algorithms for the estimation of optical flowfields in image sequences. Computer Vision, Graphics, and Image Processing, 43:150–177, 1988.
13. S. Ghosal and P. Vanĕk. A fast scalable algorithm for discontinuous optical flow estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(2):181–194, Feb. 1996.
14. F. Guichard and L. Rudin. Accurate estimation of discontinuous optical flow by minimizing divergence related functionals. In Proceedings of the International Conference on Image Processing, volume I, pages 497–500, 1996.
15. F. Heitz and P. Bouthemy. Multimodal estimation of discontinuous optical flow using markov random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(12):1217–1232, Dec. 1993.
16. B. Horn and B. Schunk. Determining optical flow. Artificial Intelligence, 17:185–203, 1981.
17. B. McCane, K. Novins, D. Crannitch, and B. Galvin. On benchmarking optical flow. Computer Vision and Image Understanding, 84(1):126–143, 2002.
18. E. Memin and P. Perez. Multiresolution Markov random field and multigrid algorithm for discontinuity preserving estimation of the optical flow. In Proceedings of the International Society for Optical Engineering, San Diego, USA, July 1995.
19. H. Nagel. Constraints for the estimation of displacement vector fields from image sequences. In International Joint Conference on Artificial Intelligence, pages 156–160, 1983.
20. H. Nagel and W. Enkelmann. An investigation of smoothness constraint for the estimation of displacement vector fiels from images sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8:565–593, 1986.
21. H.-H. Nagel. On the estimation of optical flow: relations between different approaches and some newresults. Artificial Intelligence Journal, 33:299–324, 1987.
22. P. Nési. Variational approach to optical flowestimation managing discontinuities. Image and Vision Computing, 11(7):419–439, Sept. 1993.
23. M. Otte and H. Nagel. Optical flowestimation: Advances and comparisons. In J.-O. Eklundh, editor, Proceedings of the 3rd European Conference on Computer Vision, volume 800 of Lecture Notes in Computer Science, pages 51–70. Springer– Verlag, 1994.
24. M. Proesmans, L. Van Gool, E. Pauwels, and A. Oosterlinck. Determination of Optical Flowand its Discontinuities using Non-Linear Diffusion. In Proceedings of the 3rd ECCV, II, number 801 in Lecture Notes in Computer Science, pages 295–304. Springer–Verlag, 1994.
25. G. Sapiro. Geometric Partial Differential Equations and Image Analysis. Cambridge University Press, 2001.
26. C. Schnörr. Determining optical flowfor irregular domains by minimizing quadratic functionals of a certain class. The International Journal of Computer Vision, 6(1):25–38, 1991.
27. M. Snyder. On the mathematical fundations of smoothness constraints for the determination of optical flowand for surface reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(11), Nov. 1995.
28. J. Weickert. Anisotropic Diffusion in Image Processing. PhD thesis, University of Kaiserslautern, Germany, Laboratory of Technomathematics, Jan. 1996.
29. J. Weickert. Theoretical foundations of anisotropic diffusion in image processing. Computing Supplement, 11:221–236, 1996.
30. J. Weickert. Anisotropic Diffusion in Image Processing. Teubner-Verlag, Stuttgart, 1998.
31. J. Weickert and C. Schnörr. A theoretical framework for convex regularizers in PDE-based computation of image motion. The International Journal of Computer Vision, 45(3):245–264, Dec. 2001.