A scale-space approach to nonlocal optical flow calculations

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

Volumn 1682, Issue , 1999, Pages 235-246

  Alvarez, Luis ^a   Weickert, Joachim ^b   Sánchez, Javier ^a  

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

^b UNIVERSITY OF MANNHEIM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION;

ANISOTROPIC DIFFUSION; DIGITAL IMAGE ANALYSIS; DISPLACEMENT FIELD; FLOW CALCULATIONS; MODEL FORMULATION; NUMERICAL SCHEME; OPTICAL FLOW METHODS; REAL-WORLD IMAGE;

OPTICAL FLOWS;

EID: 84948150452     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-48236-9_21     Document Type: Conference Paper

References (33)

1. L. Alvarez, J. Esclarín, M. Lefébure and J. Sánchez, A PDE model for computing the optical flow, Proc. XVI Congreso de Ecuaciones Diferenciales y Aplicaciones (C.E.D.Y.A. XVI, Las Palmas de Gran Canaria, Sept. 21-24, 1999), in press.
2. L. Alvarez, F. Guichard, P.-L. Lions, J.-M. Morel, Axioms and fundamental equations in image processing, Arch. Rational Mech. Anal., Vol. 123, 199-257, 1993.
3. L. Alvarez, P.-L. Lions, J.-M. Morel, Image selective smoothing and edge detection by nonlinear Diffusion. II, SIAM J. Numer. Anal., Vol. 29, 845-866, 1992.
4. P. Anandan, A computational framework and an algorithm for the measurement of visual motion, Int. J. Comput. Vision, Vol. 2, 283-310, 1989.
5. J.L. Barron, D.J. Fleet, S.S. Beauchemin, Performance of optical flow techniques, Int. J. Comput. Vision, Vol. 12, 43-77, 1994.
6. F. Bergholm, Edge focusing, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 9, 726-741, 1987.
7. M. Bertero, T.A. Poggio, V. Torre, Ill-posed problems in early vision, Proc. IEEE, Vol. 76, 869-889, 1988.
8. M.J. Black, P. Anandan, Robust dynamic motion estimation over time, Proc. IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition (CVPR ’91, Maui, June 3-6, 1991), IEEE Computer Society Press, Los Alamitos, 292-302, 1991.
9. A. Blake, A. Zisserman, Visual reconstruction, MIT Press, Cambridge (Mass.), 1987.
10. I. Cohen, Nonlinear variational method for optical flow computation, Proc. Eighth Scandinavian Conf. on Image Analysis (SCIA ’93, Troms_, May 25-28, 1993), Vol. 1, 523-530, 1993.
11. R. Deriche, P. Kornprobst, G. Aubert, Optical-flow estimation while preserving its discontinuities: A variational approach, Proc. Second Asian Conf. Computer Vision (ACCV ’95, Singapore, December 5-8, 1995), Vol. 2, 290-295, 1995.
12. W. Enkelmann, Investigation of multigrid algorithms for the estimation of optical flow élds in image sequences, Computer Vision, Graphics and Image Processing, Vol. 43, 150-177, 1988.
13. L. Florack, Image structure, Kluwer, Dordrecht, 1997.
14. L.M.J. Florack, W.J. Niessen, M. Nielsen, The intrinsic structure of the optic flow incorporating measurement duality, Int. J. Comput. Vision, Vol. 27, 263-286, 1998.
15. B. ter Haar Romeny, L. Florack, J. Koenderink, M. Viergever (Eds.), Scale-space theory in computer vision, Lecture Notes in Computer Science, Vol. 1252, Springer, Berlin, 1997.
16. B. Horn, B. Schunck, Determining optical flow, Artif. Intell., Vol. 17, 185-203, 1981.
17. T. Iijima, Basic theory on normalization of pattern (in case of typical onedimensional pattern), Bulletin of the Electrotechnical Laboratory, Vol. 26, 368-388, 1962 (in Japanese).
18. T. Iijima, Pattern recognition, Corona-sha, 1973 (in Japanese).
19. T. Iijima, Theory of pattern recognition, Series of Basic Information Technology, Vol. 6, Morishita Publishing, 1989 (in Japanese).
20. J.J. Koenderink, The structure of images, Biological Cybernetics, Vol. 50, 363-370, 1984.
21. A. Kumar, A.R. Tannenbaum, G.J. Balas, Optic flow: a curve evolution approach, IEEE Trans. Image Proc., Vol. 5, 598-610, 1996.
22. T. Lindeberg, Scale-space theory in computer vision, Kluwer, Boston, 1994.
23. A. Mitiche, P. Bouthemy, Computation and analysis of image motion: a synopsis of current problems and methods, Int. J. Comput. Vision, Vol. 19, 29-55, 1996.
24. H.H. Nagel, W. Enkelmann, An investigation of smoothness constraints for the estimation of displacement vector élds from images sequences, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 8, 565-593, 1986.
25. M. Proesmans, L. Van Gool, E. Pauwels, A. Oosterlinck, Determination of optical flow and its discontinuities using non-linear Diffusion, J.-O. Eklundh (Ed.), Computer vision-ECCV ’94, Lecture Notes in Computer Science, Vol. 801, Springer, Berlin, 295-304, 1994.
26. E. Radmoser, O. Scherzer, J. Weickert, Scale-space properties of regularization methods, this volume.
27. C. Schnörr, Segmentation of visual motion by minimizing convex non-quadratic functionals, Proc. 12th Int. Conf. Pattern Recognition (ICPR 12, Jerusalem, Oct. 9-13, 1994), Vol. A, IEEE Computer Society Press, Los Alamitos, 661-663, 1994.
28. J. Shah, A nonlinear Diffusion model for discontinuous disparity and half-occlusions in stereo, Proc. IEEE Comp. Soc. Conf. Computer Vision and Pattern Recognition (CVPR ’93, New York, June 15-17, 1993), IEEE Computer Society Press, Los Alamitos, 34-40, 1993.
29. J. Sporring, M. Nielsen, L. Florack, P. Johansen (Eds.), Gaussian scale-space theory, Kluwer, Dordrecht, 1997.
30. J. Weickert, Theoretical foundations of anisotropic Diffusion in image processing, Computing, Suppl. 11, 221-236, 1996.
31. J. Weickert, Anisotropic Diffusion in image processing, Teubner, Stuttgart, 1998.
32. J. Weickert, On discontinuity-preserving optic flow, S. Orphanoudakis, P. Trahanias, J. Crowley, N. Katevas (Eds.), Proc. Computer Vision and Mobile Robotics Workshop (CVMR ’98, Santorini, Sept. 17-18, 1998), 115-122, 1998.
33. J. Weickert, S. Ishikawa, A. Imiya, Linear scale-space has first been proposed in Japan, J. Math. Imag. Vision, Vol. 10, 237-252, 1999.