A Bayesian framework for regularization

Proceedings - International Conference on Pattern Recognition

Volumn 3, Issue , 1994, Pages 72-76

  Keren, Daniel ^a   Werman, Michael ^b  

^a UNIVERSITY OF HAIFA   (Israel)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; IMAGE PROCESSING;

BAYESIAN APPROACHES; BAYESIAN FRAMEWORKS; FIDELITY TERM; FUNCTION SPACES; INTERPOLATING FUNCTIONS; LINEAR FUNCTIONAL; PROBABILITY: DISTRIBUTIONS; REGULARISATION; WEIGHTED SUM;

PROBABILITY DISTRIBUTIONS;

EID: 85027179773     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.1994.577125     Document Type: Conference Paper

References (19)

1. M. Bertero, T.A Poggio, and V. Torre. Ill-posed problems in early vision. Proceedings of the IEEE, 8:869-889, 1988.
2. P. Craven and G. Whaba. Optimal smoothing of noisy data with spline functions. Numerische Mathematik, 31:377-403, 1979.
3. S. Geman and D. Geman. Stochastic relaxation, gibbs distribution, and the bayesian restorat ion of images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 6:721-741, June 1984.
4. B. Horn. Robot Vision. MIT Press, 1986.
5. B.K.P Horn and B.G. Schunck. Determining optical flow. Artificial Intelligence, 17:185-203, 1981.
6. D. Keren and M. Werman. Variations on regularization. In 10th International Conference on Pattern Recognition, Atlantic City, 1990.
7. D. Keren and M. Werman. A bayesian framework for regularization. 1994. submitted to IEEE Transaction on Pattern Analysis and Machine Intelligence.
8. D. Keren and M. Werman. Probabilistic analysis of regularization. IEEE Trans. on Pattern Analysis and Machine Intelligence, 15:982-995, October 1993.
9. Y.G. Leclerc. Image and boundary segmentation via minimal-length encoding on the co nnection machine. In Image Understanding Workshop, pages 1056-1069, 1989.
10. David J.C. MacKay. Bayesian Methods for Adaptive Models. PhD thesis, California Institute of Technology, 1992.
11. J.L. Marroquin. Deterministic bayesian estimation of markovian random fields with applications to computational vision. In International Conference on Computer Vision, pages 597-601, London, May 1987.
12. L. Matthies, R. Szeliski, and T. Kanade. Incremental estimation of dense depth maps from image sequences. In IEEE Conference on Computer Vision and Pattern Recognition, pages 366-374, Ann Arbor, June 1988.
13. D. Mumford and J. Shah. Boundary detection by minimizing fuctionals. In IEEE Conference on Computer Vision and Pattern Recognition, pages 22-26, San Francisco, June 1985.
14. R. Szeliski. Regularization uses fractal priors. In National Conference on Artificial Intelligence, pages 749-754, 1987.
15. R. Szeliski. Bayesian Modeling of Uncertainty in Low-Level Vision. Kluwer, 1989.
16. S. Szeliski and D. Terzopoulos. From splines to fractals. In SIGGRAPH, pages 51-60, 1989.
17. D. Terzopoulos. Multi-level surface reconstruction. In A. Rosenfeld, editor, Multiresolution Image Processing and Analysis. Springer-Verlag, 1984.
18. D. Terzopoulos. Regularization of visual problems involving discontinuities. IEEE Trans. on Pattern Analysis and Machine Intelligence, 8:413-424, August 1986.
19. A.N. Tikhonov and V.Y. Arsenin. Solution of Ill-Posed Problems. Winston and Sons, 1977.