The Robust Sequential Estimator: A General Approach and its Application to Surface Organization in Range Data

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 16, Issue 10, 1994, Pages 987-1001

  Boyer, Kim L ^a   Mirza, Muhammad J ^a   Ganguly, Gopa ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

perceptual organization; Range data; robust estimators; segmentation; sequential estimators; surface parameterization

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTER SIMULATION; LEAST SQUARES APPROXIMATIONS; ROBUSTNESS (CONTROL SYSTEMS);

AKAIKE INFORMATION CRITERION; ROBUST SEQUENTIAL ESTIMATOR; SEED POINT; SEGMENTATION; WEIGHTED VOTING SCHEME;

COMPUTER VISION;

EID: 0028515975     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.329010     Document Type: Article

References (28)

1. P. J. Huber, Robust Statistics. New York: John Wiley, 1981.
2. P. J. Rousseeuw and A. M. Leroy, Robust Regression and Outlier Detection. New York: John Wiley, 1987.
3. R. M. Haralick, -Computer vision theory: The lack thereof.” Comput. Vision, Graphics, and Image Processing, vol. 36, pp. 372-386, 1986.
4. W. Forstner, “Reliability analysis of parameter estimation in linear models with application to mensuration problems in computer vision,” Comput. Vision. Graphics, and Image Processing, vol. 40, pp. 273-310, 1987.
5. P. J. Besl, J. B. Birch, and L. T. Watson, “Robust window operators,” in Proc. 1988 Int. Conf. on Computer Vision (ICCV '88), 1988.
6. O. Faugeras and M. Berthod, “improving consistency and reducing am-higuity biguity in stochastic labeling: An optimization approach,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-3, pp. 412-424, July 1981.
7. P. Meer, D. Mintz, A. Rosenfeld, and D. Y. Kim, “Robust regression methods for computer vision: A review,” Int. J. Comput. Vision, vol. 6, no. 1, pp. 59-70, 1991.
8. H. Akaike, “Information theory and an extention of the maximum likelihood principle,” in Second Int. Symp. Inform. Theory, 1973, pp. 267-281.
9. M. J. Mirza and K. L. Boyer, “Performance evaluation of a class of M-estimators estimators for surface parameter estimation in noisy range data,” IEEE Trans. Robotics Automat., vol. 9, pp. 75-85, Feb, 1993.
10. M. J. Mirza and K. L. Boyer. “An information theoretic robust sequential procedure for surface order selection in noisy range data,” in Proc. 1992 IEEE Conf. Comput. Vision Pattern Recognit., June 1992, pp. 366-371.
11. P. J. Besl and R. C. Jain, “Invariant surface characteristics for 3-D object recognition in range images,” Comput. Vision, Graphics, and Image Processing. vol. 33, pp. 33-80, 1986.
12. M. Brady, J. Ponce, A. Yuille, and H. Asada, “Describing surfaces,” Comput. Vision, Graphics, and Image Processing, vol. 32, pp. 1-28, 1985.
13. T. Fan, G. Medioni, and R. Nevatia, “Description of surfaces from range data using curvature properties,” in Proc. 1986 IEEE Conf. on Comput. Vision and Pattern Recognition, May 1986, pp. 86-91.
14. R. L. Hoffman and A. K. Jain, “Segmentation and classification of range images,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-9. pp. 608-620, Sept. 1987.
15. A. K. Jain and S. G. Nadabar, “Range image segmentation using MRF models,” in Markov Random Field Models, R. Chellappa and A. K. Jain, Eds. San Diego, CA: Academic Press, 1993, pp. 543-572.
16. T. Fan, G. Medioni, and R. Nevatia, “Surface segmentation and description from curvature features,” in Proc. 1987 DARPA Image Understanding Workshop, 1987, pp. 351-359.
17. T. E. Boult and M. Lerner, “Energy-based segmentation of very sparse range surfaces,” in Proc. IEEE Int. Conf. Robotics Automat., 1990, pp. 232-237.
18. P. J. Besl and R. C. Jain, “Segmentation through variable-order surface fitting,” IEEE Trans. Pattern Anal. Machine Intell., vol. 9, no. 2, pp. 167-192, 1988.
19. M. Koch and R. Kashyap, “Using polygons to recognize and locate partially occluded objets,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-9, pp. 483-494, July 1987.
20. R. C. Boles and R. A. Cain, “Recognizing and locating partially visible object: The local-feature-focus method,” Int. J. Robotics Res., vol. 1, pp. 57-81, Fall 1982.
21. R. B. Fisher, From Surfaces to Objects: Computer Vision and Three Dimensional Scene Analysis. New York: John Wiley, 1989.
22. G. E. P. Box and N. R. Draper, Empirical Model-Balding and Response Surfaces. New York: John Wiley, 1987.
23. A. E. Beaton and J. W. Tukey, “The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data,” Technometrics. vol. 16, pp. 147-185, May 1974.
24. C. Goodall, “M-Estimator of Location: An Outline of the Theory,” in Understanding Robust and Exploratory Data Analysis, D. C. Hoaglin, F. Mosteller, and J. W. Tukey, Eds. New York: John Wiley, 1983, pp. 339-403.
25. D. Andrews, P. Bickel, F. Hampel, P. Huber, W. Rogers, and J. Tukey, Robust Estimates of Location: Survey and Advances. Princeton, NJ: Princeton Univ. Press, 1972.
26. T. C. Hsia, System Identification: Least-Squares Methods. Lexington, MA: Lexington Books, 1976.
27. H. Bozdogan, “Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions,” Psychometrika, vol. 52, no. 3, pp. 345-370, 1987.
28. Y. Shirai, Three-Dimensional Computer Vision, New York: Springer-Verlag, 1987.