MINPRAN: A New Robust Estimator for Computer Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 17, Issue 10, 1995, Pages 925-938

  Stewart, Charles V ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

outliers; parameter estimation; range data; robust estimation; Surface reconstruction

Indexed keywords

DATA ACQUISITION; IMAGE RECONSTRUCTION; IMAGE SENSORS; PARAMETER ESTIMATION; ROBUSTNESS (CONTROL SYSTEMS); SURFACES;

MINPRAN ESTIMATOR; ROBUST ESTIMATION; SURFACE RECONSTRUCTION;

COMPUTER VISION;

EID: 0029391811     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.464558     Document Type: Article

References (34)

1. M. Abramowitz and I.A. Stegum, Handbook of Mathematical Functions. Dover Publications, 1968.
2. D.H. Ballard and C.M. Brown, Computer Vision. Prentice Hall, 1982.
3. P.J. Besl, J.B. Birch, and L.T. Watson, “Robust window operators,” Proc. IEEE Int'l Conf. Computer Vision, pp. 591–600, 1988.
4. P.J. Besl and R.C. Jain, “Segmentation through variable-order surface fitting,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 10, pp. 167–192, 1988.
5. T. Boult and M. Lerner, “Energy-based segmentation of very sparse range surfaces,” Proc. IEEE Int'l Conf. Robotics and Automation, pp. 232–237. 1990
6. K.L. Boyer, M.J. Mirza, and G. Ganguly, “The robust sequential estimator: A general approach and its application to surface organization in range data,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, pp. 987–1,001, 1994.
7. H.A. David, Order Statistics. John Wiley & Sons, 1970.
8. M.A. Fischler and R.C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” CACM, vol. 24, pp. 381–395, 1981.
9. W.E.L. Grimson and D. Huttenlocher, “On the sensitivity of the Hough transform for object recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, pp. 255–274, 1990.
10. J. Illingworth and J. Kittler, “A survey of the Hough transform,” CVGIP, vol. 44, pp. 87–116, 1988.
11. J.-M. Jolion, P. Meer, and S. Bataouche, “Robust clustering with applications in computer vision,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, pp. 791–802, 1991.
12. R. Kumat and A.R. Hanson, “Robust methods for estimating pose and a sensitivity analysis,” CVGIP: Image Understanding, 1994.
13. A. Leonardis, A. Gupta, and R. Bajcsy, “Segmentation as the search for the best description of the image in terms of primitives,” Proc. IEEE Int'l Conf. Computer Vision, pp. 121–125, 1990.
14. S. Madarasmi, D. Kersten, and T.C. Pong, “Multilayer surface segmentation using energy minimization,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 774–775, 1993.
15. P. Meer, D. Mintz, A. Rosenfeld, and D.Y. Kim, “Robust regression methods for computer vision: A review,” Int'l J. Computer Vision, vol. 6, pp. 59–70, 1991.
16. D. Mintz, “Robustness by consensus,” Technical Rep. CAR-TR-576, Univ. of Maryland—Center for Automation Research, 1991.
17. D.F. Morrison, Applied Linear Statistical Methods. Prentice Hall, 1983.
18. A. Papoulis, Probability, Random Variables, and Stochastic Processes. McGraw-Hill, 1984.
19. G. Roth and M.D. Levine, “Extracting geometric primitives,” CVGIP: Image Understanding, vol. 58, pp. 1–22, 1993.
20. P.J. Rousseeuw, “Least median of squares regression,” J. American Statistical Assoc., vol. 79, pp. 871–880, 1984.
21. P.J. Rousseeuw and A.M. Leroy, Robust Regression and Outlier Detection. John Wiley & Sons, 1987.
22. K. Sato and S. Inokuchi, “Range-imaging system utilizing nematic liquid crystal mask,” Proc. IEEE Int'l Conf. Computer Vision, pp. 657–661, 1987.
23. B.G. Schunck, “Robust computational vision,” Proc. Int'l Workshop Robust Computer Vision, pp. 1–18, 1990.
24. S.D. Silvey, Statistical Inference. John Wiley & Sons, 1975.
25. S.S. Sinha and B.G. Schunck, “A two-stage algorithm for discontinuity-perserving surface reconstruction,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, pp. 36–55, 1992.
26. D.L. Souvaine and J.M. Steele, “Time- and space-efficient algorithms for least median of squares regression,” J. American Statistical Assoc., vol. 82, pp. 794–801, 1987.
27. A. Stein and M. Werman, “Robust statistics in shape fitting,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 540–546, 1992.
28. C.V. Stewart, “Expected performance of robust estimators near discontinuities,” Tech. Rep. 94–10, Dept. of Computer Science, Rensselaer Polytechnic Inst., 1994.
29. C.V. Stewart, “A new robust operator for computer vision: Application to range images,” Proc. IEEE Conf. Computer Vision and Pattern Recognition pp. 167–173, 1994.
30. C.V. Stewart, “A new robust operator for computer vision: Theoretical analysis,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1–8, 1994.
31. C.V. Stewart, “A new robust operator for computer vision: Theoretical analysis,” Technical Rep. 93–21, Dept. of Computer Science, Rensselaer Polytechnic Inst., Aug. 1993.
32. C.V. Stewart, “A new robust operator for computer vision: Application to range and intensity images,” Technical Rep. 93–24, Dept. of Computer Science, Rensselaer Polytechnic Inst., Oct. 1993.
33. X. Zhuang and R.M. Haralick, “Developing robust techniques for computer vision,” Proc. Int'l Workshop Robust Computer Vision, pp. 19–38, 1990.
34. X. Zhuang, T. Wang, and P. Zhang, “A highly robust estimator through partially likelihood function modeling and its application in computer vision,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, pp. 19–35, 1992.