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Cited by 2,570

The DOI matches its Crossref record.

High Accuracy Optical Flow Estimation Based on a Theory for Warping

Brox, Thomas, Bruhn, Andrés, Papenberg, Nils, Weickert, Joachim·2004·Lecture Notes in Computer Science
DOI 10.1007/978-3-540-24673-2_3
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Research works printed but not linked: 25

  1. Alvarez, L., Esclarin, J., Lefebure, M., Sanchez, J.: A PDE model for computing the optical flow. In: Proc. XVI Congreso de Ecuaciones Diferenciales y Aplicaciones, Las Palmas de Gran Canaria, Spain, September 1999, pp. 1349–1356 (1999)
  2. Alvarez, L., Weickert, J., Sanchez, J.: Reliable estimation of dense optical flow fields with large displacements. International Journal of Computer Vision 39(1), 41–56 (2000) · DOI · Paper page
  3. Anandan, P.: A computational framework and an algorithm for the measurement of visual motion. International Journal of Computer Vision 2, 283–310 (1989) · DOI · Paper page
  4. Bab-Hadiashar, A., Suter, D.: Robust optic flow computation. International Journal of Computer Vision 29(1), 59–77 (1998) · DOI
  5. Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 43–77 (1994) · DOI · Paper page
  6. Black, M.J., Anandan, P.: Robust dynamic motion estimation over time. In: Proc. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Maui, HI, June 1991, pp. 292–302. IEEE Computer Society Press, Los Alamitos (1991)
  7. 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) · DOI · Paper page
  8. Cohen, I.: Nonlinear variational method for optical flow computation. In: Proc. Eighth Scan-dinavian Conference on Image Analysis, Tromsø, Norway, May 1993, vol. 1, pp. 523–530 (1993)
  9. Deriche, R., Kornprobst, P., Aubert, G.: Optical-flow estimation while preserving its dis-continuities: a variational approach. In: Li, S., Teoh, E.-K., Mital, D., Wang, H. (eds.) ACCV 1995. LNCS, vol. 1035, pp. 290–295. Springer, Heidelberg (1995)
  10. Farnebáck, G.: Very high accuracy velocity estimation using orientation tensors, parametric motion, and simultaneous segmentation of the motion field. In: Proc. Eighth International Conference on Computer Vision, Vancouver, Canada, July 2001, vol. 1, pp. 171–177. IEEE Computer Society Press, Los Alamitos (2001) · DOI
  11. Horn, B., Schunck, B.: Determining optical flow. Artificial lntelligence 17, 185–203 (1981) · DOI · Paper page
  12. Ju, S., Black, M., Jepson, A.: Skin and bones: multi-layer, locally affine, optical flow and regularization with transparency. In: Proc. 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, June 1996, pp. 307–314. IEEE Computer Society Press, Los Alamitos (1996)
  13. Lai, S.-H., Vemuri, B.C.: Reliable and efficient computation of optical flow. International Journal of Computer Vision 29(2), 87–105 (1998) · DOI
  14. Lefeburé, M., Cohen, L.D.: Image registration, optical flow and local rigidity. Journal of Mathematical Imaging and Vision 14(2), 131–147 (2001) · DOI
  15. Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. Seventh International Joint Conference on Artificial Intelligence, Vancouver, Canada, August 1981, pp. 674–679 (1981)
  16. Mémin, E., Peréz, P.: A multigrid approach for hierarchical motion estimation. In: Proc. Sixth International Conference on Computer Vision, Bombay, India, January 1998, pp. 933–938. Narosa Publishing House, Bombay (1998)
  17. Mémin, E., Peréz, P.: Hierarchical estimation and segmentation of dense motion fields. International Journal of Computer Vision 46(2), 129–155 (2002) · DOI
  18. Nagel, H.-H.: Extending the ’oriented smoothness constraint’ into the temporal domain and the estimation of derivatives of optical flow. In: Faugeras, O. (ed.) ECCV 1990. LNCS, vol. 427, pp. 139–148. Springer, Heidelberg (1990) · DOI
  19. Nagel, H.-H., Enkelmann, W.: 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 (1986) · DOI · Paper page
  20. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992) · DOI · Paper page
  21. Schnörr, C.: Segmentation of visual motion by minimizing convex non-quadratic functionals. In: Proc. Twelfth International Conference on Pattern Recognition, Jerusalem, Israel, October 1994, vol. A, pp. 661–663. IEEE Computer Society Press, Los Alamitos (1994) · DOI
  22. Tistarelli, M.: Multiple constraints for optical flow. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 800, pp. 61–70. Springer, Heidelberg (1994) · DOI
  23. Uras, S., Girosi, F., Verri, A., Torre, V.: A computational approach to motion perception. Biological Cybernetics 60, 79–87 (1988) · DOI
  24. Weickert, J., Schnörr, C.: A theoretical framework for convex regularizers in PDE-based computation of image motion. International Journal of Computer Vision 45(3), 245–264 (2001) · DOI · Paper page
  25. 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) · DOI

Cited by

Metadata

Authors
Brox, T., Bruhn, A., Papenberg, N., & Weickert, J.
Venue
Lecture Notes in Computer Science
Publisher
Springer Berlin Heidelberg
Published
2004
Pages
25-36
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-540-24673-2_3
OpenAlex
W1867429401
Cited by
2,570
References
0
FWCI
35.06
Type
conference paper
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)

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