A generative model based approach to motion segmentation

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

Volumn 2781, Issue , 2003, Pages 313-320

  Cremers, Daniel ^a   Yuille, Alan ^b  

^a University of California   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; INFERENCE ENGINES; PATTERN RECOGNITION;

BAYESIAN INFERENCE; LEVEL SET FRAMEWORK; LIKELIHOOD FUNCTIONS; MEASUREMENT PROCESS; MOTION DISCONTINUITIES; MOTION SEGMENTATION; PIECE-WISE CONSTANTS; VARIATIONAL FRAMEWORK;

MOTION ESTIMATION;

EID: 21144432940     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-45243-0_41     Document Type: Article

References (16)

1. M. J. Black and P. Anandan. The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields. Comp. Vis. Graph. Image Proc.: IU, 63(1):75-104, 1996.
2. D. Cremers. A variational framework for image segmentation combining motion estimation and shape regularization. In C. Dyer and P. Perona, editors, IEEE Int. Conf. on Comp. Vis. and Patt. Recog., Madison, Wisconsin, June 2003. To appear.
3. D. Cremers and C. Schnörr. Motion Competition: Variational integration of motion segmentation and shape regularization. In L. van Gool, editor, Pattern Recognition, volume 2449 of LNCS, pages 472-480, Zürich, Sept. 2002. Springer.
4. D. Cremers and S. Soatto. Combining motion estimation and surface regularization for Variational image sequence segmentation. In ICCV, Nice, Oct. 2003. To appear.
5. B.K.P. Horn and B.G. Schunck. Determining optical flow. A.I., 17:185-203, 1981.
6. T. Kohlberger, E. Memin, and C. Schnörr. Variational dense motion estimation using the Helmholtz decomposition. In L. Griffin, editor, Int. Conf. on Scale Space Theories in Comp. Vis., LNCS. Springer, 2003. To appear.
7. P. Kornprobst, R. Deriche, and G. Aubert. Image sequence analysis via partial differential equations. J. Math. Im. Vis., 11(1):5-26, 1999.
8. H.H. Nagel. Optical flow estimation and the interaction between measurement errors at adjacent pixel positions. Int. J. of Comp. Vis., 15(3):271-288, 1995.
9. H.H. Nagel and W. Enkelmann. An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Trans. on Patt. Anal. and Mach. Intell., 8(5):565-593, 1986.
10. O. Nestares, D.J. Fleet, and D.J. Heeger. Likelihood functions and confidence bounds for total-least-squares problems. In Proc. Conf. Computer Vis. and Pattern Recog., volume 1, pages 760-767, Hilton Head Island, SC, June, 2000.
11. S. J. Osher and J. A. Sethian. Fronts propagation with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulations. J. of Comp. Phys., 79:12-49, 1988.
12. C. Schnorr. Computation of discontinuous optical flow by domain decomposition and shape optimization. Int. J. of Comp. Vis., 8(2):153-165, 1992.
13. E.P. Simoncelli. Distributed Representation and Analysis of Visual Motion. PhD thesis, Dept. of Elect. Eng. and Comp. Sci., MIT, Cambridge, 1993.
14. E.P. Simoncelli, E.H. Adelson, and D.J. Heeger. Probability distributions of optical flow. In Proc. Conf. Comp. Vis. Patt. Rec., pages 310-315, Hawaii, June 1991.
15. J. Weickert and C. Schnörr. A theoretical framework for convex regularizers in PDE-based computation of image motion. IJCV, 45(3):245-264, 2001.
16. Y. Weiss and D.J. Fleet. Velocity likelihoods in biological and machine vision. In M.S. Lewicki R.P.N. Rao, B.A. Olshausen, editor, Probabilistic Models of the Brain: Perception and Neural Function, pages 81-100. MIT Press, 2001.