Multimodal Estimation of Discontinuous Optical Flow Using Markov Random Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 15, Issue 12, 1993, Pages 1217-1232

  Heitz, Fabrice ^a   Bouthemy, Patrick ^a  

^a CAMPUS DE BEAULIEU   (France)

Author keywords

deterministic relaxation; discontinuities in optical flow; MAP estimate; Markov Random Fields; multiple constraints; multiresolution analysis; occlusion processing; Visual motion analysis

Indexed keywords

ALGORITHMS; CONSTRAINT THEORY; IMAGE ANALYSIS; IMAGE PROCESSING; MATHEMATICAL MODELS; MOTION CONTROL; OPTICAL FLOWS; PARAMETER ESTIMATION; RANDOM PROCESSES; VISION;

BAYESIAN ESTIMATION; DETERMINISTIC RELAXATION; DISCONTINUITIES IN OPTICAL FLOW; MARKOV RANDOM FIELD; MULTIMODAL ESTIMATION; MULTIPLE CONSTRAINT; MULTIRESOLUTION ANALYSIS; OCCLUSION PROCESSING; VELOCITY FIELD; VISUAL MOTION ANALYSIS;

PATTERN RECOGNITION;

EID: 0027807652     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.250841     Document Type: Article

References (35)

1. G. Adiv,. “Determining three-dimensional motion and structure from optical flow generated by several moving objects,” IEEE Trans. Pattern Anal. Machine Intell., vol. 7, pp. 384–401, July 1985.
2. J. K. Aggarwal and N. Nandhakumar, “On the computation of motion from sequences of images—A review,” Proc. IEEE, vol. 76, no. 8, pp. 917–935, 1988.
3. S. T. Barnard, “Stochastic stereo matching over scale,” Int. J. Comput. Vision, vol. 3, pp. 17–32, 1989.
4. J. Bergen, P. Burt, R. Hingorani, and S. Peleg, “Computing two motions from three frames,” in Proc. 3rd Int. Conf. Comput. Vision, Osaka, Dec. 1990, pp. 27–32.
5. J. Besag, “On the statistical analysis of dirty pictures,” J. Roy. Statist. Soc., vol. 48, ser. B, no 3, 259–302, 1986.
6. M. J. Black and P. Anandan, “A model for the detection of motion over time,” in Proc. 3rd Int. Conf. Comput. Vision, Osaka, Dec. 1990, pp. 33–37.
7. P. Bouthemy, “A maximum-likelihood framework for determining moving edges,” IEEE Trans. Pattern Anal. Machine Intell., vol. 11, no. 5, pp. 499–511, May 1989.
8. P. Bouthemy and E. Francois, “Motion segmentation and qualitative dynamic scene analysis from an image sequence,” Int. J. Comput. Vision, vol. 10, no. 2, pp. 157–182, 1993.
9. P. Bouthemy and P. Lalande, “Detection and tracking of moving objects based on a statistical regularization method in space and time,” in Proc. First European Conf. Comput. Vision, Antibes, France, Apr. 1990, pp. 307–311.
10. R. Deriche, “Using Canny’s criteria to derive a recursively implemented optimal edge detector,” Int. J. Comput. Vision, 167–187, 1987.
11. W. Enkelmann, “Investigations of multigrid algorithms for the estimation of optical flow fields in image sequences,” Comput. Vision, Graphics, Image Processing, vol. 43, pp. 150–177, 1988.
12. E. Gamble and T. Poggio, “Visual integration and detection of discontinuities: The key role of intensity edges,” Tech. Rep. A.I. 970, M.I.T., Oct. 1987.
13. D. Geman, S. Geman, C. Graffigne, and D. Pong, “Boundary detection by constrained optimization,” IEEE Trans. Pattern Anal. Machine Intell, vol. 12, no. 7, pp. 609–628, July 1990.
14. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions and the bayesian restoration of images,” IEEE Trans. Pattern Anal. Machine Intell., vol. 6, no. 6, pp. 721–741, Nov. 1984.
15. F. Heitz and P. Bouthemy, “Multimodal motion estimation and segmentation using Markov random fields,” in Proc. 10th Int. Conf. Pattern Recognit., vol. 1, Atlantic City, NJ, June 1990, pp. 378–383.
16. F. Heitz and P. Bouthemy, “Multimodal estimation of discontinuous optical flow using Markov random fields,” Tech. Rep. 1367, INRIA-Rennes, Jan. 1991.
17. F. Heitz, P. Perez, and P. Bouthemy, “Multiscale minimization of global energy functions in some visual recovery problems,” CVGIP: Image Understanding, accepted for publication, 1993.
18. B. K. P. Horn and B. G. Schunck, “Determining optical flow,” Artificial Intell., vol. 17, pp. 185–203, 1981.
19. J. Hutchinson, C. Koch, J. Luo, and C. Mead, “Computing motion using analog and binary resistive networks,” Comput., vol. 21, pp. 52–63, Mar. 1988.
20. J. K. Kearney, W. B. Thompson, and D. L. Boley, “Optical flow estimation: An error analysis of gradient-based methods with local optimization,” IEEE Trans. Pattern Anal. Machine Intel., vol. 9, no. 2, 229–244, 1987.
21. J. Konrad and E. Dubois, “Estimation of image motion fields: Bayesian formulation and stochastic solution,” in Proc. Int. Conf. Acoust., Speech, Signal Processing, New York, 1988, pp. 1072–1075.
22. J. Konrad and E. Dubois, “Bayesian estimation of motion vector fields,” IEEE Trans. Pattern Anal. Machine Intell., vol. 14, no. 9, pp. 910–927, 1992.
23. J. J. Little and W. E. Gillett, “Direct evidence for occlusion in stereo and motion,” in Proc. First European Conf. Comput. Vision, Antibes, France, Apr. 1990, pp. 336–340.
24. A. Mitiche, Y. F. Wang, and J. K. Aggarwal, “Experiments in computing optical flow with the gradient-based, multiconstraint method,” Pattern Recognit., vol. 20, no. 2, pp. 173–179, 1987.
25. D. W. Murray and H. Buxton, “Scene segmentation from visual motion using global optimization,” IEEE Trans. Pattern Anal Machine Intell., vol. 9, no. 2, pp. 220–228, Mar. 1987.
26. H. H. Nagel, “On the estimation of optical flow: Relations between different approaches and some new result,” Artificial Intell., vol. 33, p. 299–324, 1987.
27. H. H. Nagel and W. Enkelmann, “An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences,” IEEE Trans. Pattern Anal. Machine Intell., vol. 8, pp. 565–593, 1986.
28. S. Peleg and H. Rom, “Motion based segmentation,” in Proc. 10th Int. Conf. Pattern Recognit., vol. 1, Atlantic City, NJ, June 1990, pp. 109-113:
29. B. G. Schunck, “Image flow segmentation and estimation by constraint line clustering,” IEEE Trans. Pattern Anal. Machine Intell., vol. 11, no. 10, 1010–1027, Oct. 1989.
30. M. Shizawa and K. Mase, “Simultaneous multiple optical flow estimation,” in Proc. 10th Int. Conf Pattern Recognit., vol. 1, Atlantic City, NJ, June 1990, pp. 274–278.
31. A. Singh, “An estimation-theoretic framework for image flow computation,” in Proc. 3rd Int. Conf Comput. Vision, Osaka, Dec. 1990, pp. 168-177.
32. A. Spoerri and S. Ullman, “The early detection of motion boundaries,” in Proc. First Int. Conf: Comput. Vision, London, U.K., June 1987, pp. 209-218.
33. D. Terzopoulos, “Image analysis using multigrid relaxation methods,” IEEE Trans. Pattern Anal. Machine Intell., vol. 8, no. 2, pp. 129-139, Mar. 1986.
34. A. Verri and T. Poggio, “Motion field and optical flow: Qualitative properties,” IEEE Trans. Pattern Anal. Machine Intell., vol. 11, no. 5, pp. 490-498, May 1989.
35. K. Wohn and A. M. Waxman, “The analytic structure of image flows: deformation and segmentation,” Comput. Vision, Graphics, Image Processing, vol. 49, pp. 127-151, 1990.