Toward a statistically optimal method for estimating geometric relations from noisy data: Cases of linear relations

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 1, Issue , 2003, Pages

  Okatani, Takayuki ^a   Deguchi, Koichiro ^a  

^a TOHOKU UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

MAXIMUM LIKELIHOOD ESTIMATION; OPTIMIZATION; PARAMETER ESTIMATION; STATISTICAL METHODS;

NUISANCE PARAMETER PROBLEM; OPTICAL FLOW ESTIMATION;

COMPUTER VISION;

EID: 0043016327     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. J. Neyman and E. Scott. Consistent estimates based on partially consistent observations. Econometrica, 16(1):1-32, 1948.
2. K. Kanatani. Statistical bias of conic fitting and renormalization. IEEE Transaction on Pattern Analysis and Machine Intelligence, 16(3):320-326, 1994.
3. M. Werman and D. Keren. A Bayesian method for fitting parametric and nonparametric models to noisy data. IEEE Transaction on Pattern Analysis and Machine Intelligence, 23(5):528-534, 2001.
4. P. H. S. Torr. Bayesian model estimation and selection for epipolar geometry and generic manifold fitting. International Journal of Computer Vision, 50(1):35-61, 2002.
5. V. P. Godambe. Conditional likelihood and unconditional optimum estimating equations. Biometrika, 63(2):277-284, 1976.
6. B. Lindsay. Conditional score functions: Some optimality results. Biometrika, 69(3):503-512, 1982.
7. P. J. Bickel, C. A. J. Klaassen, Y. Ritov, and J. A. Wellner. Efficient and adaptive estimation for semiparametric models. The Johns Hopkins University Press, 1993.
8. S. Amari and M. Kawanabe. Information geometry of estimating functions in semiparameteric statistical models. Technical Report METR93-20, University of Tokyo, 1993.
9. K. Kanatani. Model selection for geometric inference. In Proceedings of Asian Conference on Computer Vision, 2002.
10. Y. Leedan and P. Meer. Heteroscedastic regression in computer vision: Problems with bilinear constraint. International Journal of Computer Vision, 37:127-150, 2000.
11. B. Matei and P. Meer. A general method for errors-invariables problems in computer vision. In Proceedings of IEEE Computer Vision and Pattern Recognition, pages 18-25, 2000.
12. S. Amari and M. Kawanabe. Estimation of linear relations: Is the least square method the best? (in Japanese). Bulletin of the Japan Society for Industrial and Applied Mathematics, 1996.
13. K. Kanatani. Statistical Optimization for Geometric Computation: Theory and Practice. Elsevier, 1996.
14. P. Waterman. Projective score methods. PhD thesis, The Pennsylvania State University, 1993.
15. V.P. Godambe, editor. Estimating functions. Oxford University Press, 1991.