Optic flow in harmony

International Journal of Computer Vision

Volumn 93, Issue 3, 2011, Pages 368-388

  Zimmer, Henning ^a   Bruhn, Andrés ^a   Weickert, Joachim ^a  

^a SAARLAND UNIVERSITY   (Germany)

Author keywords

Anisotropic smoothing; Motion tensor; Optic flow; Parameter selection; Robust penalisation; Variational methods

Indexed keywords

ANISOTROPIC SMOOTHING; MOTION TENSOR; OPTIC FLOW; PARAMETER SELECTION; ROBUST PENALISATION; VARIATIONAL METHODS;

ANISOTROPY;

OPTIMIZATION;

EID: 79958761984     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-011-0422-6     Document Type: Article

References (54)

1. Alvarez, L., Esclarín, J., Lefébure, M., & Sánchez, J. (1999). A PDE model for computing the optical flow. In Proc. XVI congreso de ecuaciones diferenciales y aplicaciones (pp. 1349-1356). Las Palmas de Gran Canaria, Spain.
2. Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2009). A database and evaluation methodology for optical flow. Tech. Rep. MSR-TR-2009-179, Microsoft Research, Redmond, WA.
3. Barron, J. L., Fleet, D. J., & Beauchemin, S. S. (1994). Performance of optical flow techniques. International Journal of Computer Vision, 12(1), 43-77.
4. Bertero, M., Poggio, TA, & Torre, V. (1988). Ill-posed problems in early vision. Proceedings of the IEEE, 76(8), 869-889.
5. Bigün, J., Granlund, G. H., & Wiklund, J. (1991). Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(8), 775-790. (Pubitemid 21704202)
6. Black, M. J., & Anandan, P. (1996). The robust estimation of multiple motions: parametric and piecewise smooth flow fields. Computer Vision and Image Understanding, 63(1), 75-104. (Pubitemid 126342808)
7. Blake, A., & Zisserman, A. (1987). Visual reconstruction. Cambridge: MIT Press.
8. Brox, T., Bruhn, A., Papenberg, N., & Weickert, J. (2004). High accuracy optical flow estimation based on a theory for warping. In T. Pajdla & J. Matas (Eds.), Computer vision - ECCV 2004, Part IV. Lecture notes in computer science (Vol. 3024, pp. 25-36). Berlin: Springer.
9. Bruhn, A., & Weickert, J. (2005). Towards ultimate motion estimation: combining highest accuracy with real-time performance. In Proc. tenth international conference on computer vision (Vol. 1, pp. 749-755). Beijing: IEEE Computer Society Press. (Pubitemid 44055030)
10. Bruhn, A., Weickert, J., & Schnörr, C. (2005). Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. International Journal of Computer Vision, 61(3), 211-231.
11. Bruhn, A., Weickert, J., Kohlberger, T., & Schnörr, C. (2006). A multigrid platform for real-time motion computation with discontinuity- preserving variational methods. International Journal of Computer Vision, 70(3), 257-277. (Pubitemid 44131407)
12. Charbonnier, P., Blanc-Féraud, L., Aubert, G., & Barlaud, M. (1994). Two deterministic half-quadratic regularization algorithms for computed imaging. In Proc. IEEE international conference on image processing (Vol. 2, pp. 168-172). Austin: IEEE Computer Society Press.
13. Cohen, I. (1993). Nonlinear variational method for optical flow computation. In Proc. eighth Scandinavian conference on image analysis (Vol. 1, pp. 523-530). Norway, Tromsø.
14. Coons, S. A. (1967). Surfaces for computer aided design of space forms (Tech. Rep. MIT/LCS/TR-41). Massachusetts Institute of Technology, Cambridge.
15. Elsgolc, L. E. (1962). Calculus of variations. London: Pergamon.
16. Fleet, D. J., & Jepson, A. D. (1990). Computation of component image velocity from local phase information. International Journal of Computer Vision, 5(1), 77-104.
17. Förstner, W., & Gülch, E. (1987). A fast operator for detection and precise location of distinct points, corners and centres of circular features. In Proc. ISPRS intercommission conference on fast processing of photogrammetric data (pp. 281-305). Interlaken, Switzerland.
18. Golland, P., & Bruckstein, A. M. (1997). Motion from color. Computer Vision and Image Understanding, 68(3), 346-362. (Pubitemid 127433240)
19. Grossauer, H., & Thoman, P. (2008). GPU-based multigrid: real-time performance in high resolution nonlinear image processing. In A. Gasteratos, M. Vincze, & J. K. Tsotsos (Eds.), Lecture notes in computer science: Vol. 5008. Computer vision systems (pp. 141-150). Berlin: Springer. (Pubitemid 351775962)
20. Gwosdek, P., Zimmer, H., Grewenig, S., Bruhn, A., & Weickert, J. (2010). A highly efficient GPU implementation for variational optic flow based on the Euler-Lagrange framework. In Proc. 2010 ECCV workshop on computer vision with GPUs, Heraklion, Greece.
21. Horn, B., & Schunck, B. (1981). Determining optical flow. Artificial Intelligence, 17, 185-203.
22. Krajsek, K., & Mester, R. (2007). Bayesian model selection for optical flow estimation. In F. A. Hamprecht, C. Schnörr, & B. Jähne (Eds.), Pattern recognition. Lecture notes in computer science (pp. 142-151). Berlin: Springer.
23. Lai, S. H., & Vemuri, B. C. (1998). Reliable and efficient computation of optical flow. International Journal of Computer Vision, 29(2), 87-105. (Pubitemid 128517906)
24. Lei, C., & Yang, Y. H. (2009). Optical flow estimation on coarse-tofine region-trees using discrete optimization. In Proc. 2009 IEEE international conference on computer vision. Kyoto: IEEE Computer Society Press.
25. Lucas, B., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In Proc. seventh international joint conference on artificial intelligence (pp. 674-679). Vancouver, Canada. (Pubitemid 14478744)
26. Mileva, Y., Bruhn, A., & Weickert, J. (2007). Illumination-robust variational optical flow with photometric invariants. In F. A. Hamprecht, C. Schnörr, & B. Jähne (Eds.), Pattern recognition. Lecture notes in computer science (pp. 152-162). Berlin: Springer.
27. Murray, D. W., & Buxton, B. F. (1987). Scene segmentation from visual motion using global optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(2), 220-228.
28. Nagel, H. H. (1990). Extending the 'oriented smoothness constraint' into the temporal domain and the estimation of derivatives of optical flow. In O. Faugeras (Ed.), Lecture notes in computer science: Vol. 427. Computer vision - ECCV '90 (pp. 139-148). Berlin: Springer.
29. Nagel, H. H., & Enkelmann, W. (1986). An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 565-593.
30. Ng, L., & Solo, V. (1997). A data-driven method for choosing smoothing parameters in optical flow problems. In Proc. 1997 IEEE international conference on image processing (Vol. 3, pp. 360-363). Los Alamitos: IEEE Computer Society.
31. Perona, P., & Malik, J. (1990). Scale space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 629-639. (Pubitemid 20726016)
32. Rudin, L. I., Osher, S., & Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D, 60, 259-268.
33. Schnörr, C. (1993). On functionals with greyvalue-controlled smoothness terms for determining optical flow. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10), 1074-1079.
34. Schnörr, C. (1994). Segmentation of visual motion by minimizing convex non-quadratic functionals. In Proc. twelfth international conference on pattern recognition (Vol. A, pp. 661-663). Jerusalem: IEEE Computer Society Press.
35. Schoenemann, T., & Cremers, D. (2006). Near real-time motion segmentation using graph cuts. In K. Franke, K. R. Müller, B. Nickolay, & R. Schäfer (Eds.), Pattern recognition, Lecture notes in computer science (pp. 455-464). Berlin: Springer. (Pubitemid 44608891)
36. Shulman, D., & Hervé, J. (1989). Regularization of discontinuous flow fields. In Proc. workshop on visual motion (pp. 81-86). Irvine: IEEE Computer Society Press.
37. Simoncelli, E. P., Adelson, E. H., & Heeger, D. J. (1991). Probability distributions of optical flow. In Proc. 1991 IEEE computer society conference on computer vision and pattern recognition (pp. 310-315). Maui: IEEE Computer Society Press.
38. Sun, D., Roth, S., Lewis, J. P., & Black, M. J. (2008). Learning optical flow. In D. Forsyth, P. Torr, & A. Zisserman (Eds.), Computer vision - ECCV 2008, Part III, Lecture notes in computer science (pp. 83-97). Berlin: Springer.
39. Sun, D., Roth, S., & Black, M. J. (2010). Secrets of optical flow estimation and their principles. In Proc. 2010 IEEE computer society conference on computer vision and pattern recognition. San Francisco: IEEE Computer Society Press.
40. Sundaram, N., Brox, T., & Keutzer, K. (2010). Dense point trajectories by GPU-accelerated large displacement optical flow. In Lecture notes in computer science: Vol. 6311. Computer vision - ECCV 2010 (pp. 438-451). Berlin: Springer.
41. Tretiak, O., & Pastor, L. (1984). Velocity estimation from image sequences with second order differential operators. In Proc. seventh international conference on pattern recognition (pp. 16-19). Montreal, Canada. (Pubitemid 15517331)
42. van de Weijer, J., & Gevers, T. (2004). Robust optical flow from photometric invariants. In Proc. 2004 IEEE international conference on image processing (Vol. 3, pp. 1835-1838). Singapore: IEEE Signal Processing Society. (Pubitemid 40820590)
43. 1 optical flow computation. In D. Cremers, B. Rosenhahn, A. L. Yuille, & F. R. Schmidt (Eds.), Lecture notes in computer science: Vol. 5604. Statistical and geometrical approaches to visual motion analysis (pp. 23-45). Berlin: Springer.
44. Wedel, A., Cremers, D., Pock, T., & Bischof, H. (2009). Structure-and motion-adaptive regularization for high accuracy optic flow. In Proc. 2009 IEEE international conference on computer vision. Kyoto: IEEE Computer Society Press.
45. Weickert, J. (1996). Theoretical foundations of anisotropic diffusion in image processing. Computing Supplement, 11, 221-236.
46. Weickert, J., & Schnörr, C. (2001a). A theoretical framework for convex regularizers in PDE-based computation of image motion. International Journal of Computer Vision, 45(3), 245-264. (Pubitemid 33128009)
47. Weickert, J., & Schnörr, C. (2001b). Variational optic flow computation with a spatio-temporal smoothness constraint. Journal of Mathematical Imaging and Vision, 14(3), 245-255. (Pubitemid 32638802)
48. Werlberger, M., Pock, T., & Bischof, H. (2010). Motion estimation with non-local total variation regularization. In Proc. 2010 IEEE computer society conference on computer vision and pattern recognition. San Francisco: IEEE Computer Society Press.
49. Xiao, J., Cheng, H., Sawhney, H., Rao, C., & Isnardi, M. (2006). Bilateral filtering-based optical flow estimation with occlusion detection. In A. Leonardis, H. Bischof, & A. Pinz (Eds.), Lecture notes in computer science: Vol. 3951. Computer vision - ECCV 2006, Part I (pp. 211-224). Berlin: Springer. (Pubitemid 44029780)
50. Xu, L., Jia, J., & Matsushita, Y. (2010). Motion detail preserving optical flow estimation. In Proc. 2010 IEEE computer society conference on computer vision and pattern recognition. San Francisco: IEEE Computer Society Press.
51. Yaroslavsky, L. P. (1985). Digital picture processing: an introduction. Berlin: Springer.
52. Yoon, K. J., & Kweon, I. S. (2006). Adaptive support-weight approach for correspondence search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), 650-656. (Pubitemid 43264662)
53. 1 optical flow. In F. Hamprecht, C. Schnörr, & B. Jähne (Eds.), Lecture notes in computer science: Vol. 4713. Pattern recognition (pp. 214-223). Berlin: Springer.
54. Zimmer, H., Bruhn, A., Weickert, J., Valgaerts, L., Salgado, A., Rosenhahn, B., & Seidel, H. P. (2009). Complementary optic flow. In D. Cremers, Y. Boykov, A. Blake, & F. R. Schmidt (Eds.), Lecture notes in computer science: Vol. 5681. Energy minimization methods in computer vision and pattern recognition (EMMCVPR) (pp. 207-220). Berlin: Springer.