[1] J. Addison . Pleasures of imagination. Spectator, 6(411), June 21, 1712.
[2] G. Antille and H. El May . The use of slices in the LMS and the method of density slices: Foundation and comparison. In Y. Dodge and J. Whittaker (Eds.), Proc. 10th Symp. Computat. Statist., Neuchatel, volume I, pages 441-445. Physica-Verlag, 1992.
[3] T. Arbel and F. P. Ferrie . On sequential accumulation of evidence. Int'l J. of Computer Vision, 43: 205-230, 2001.
[4] P. J. Besl, J. B. Birch, and L. T. Watson . Robust window operators. In Proc. Int'l Conf. on Computer Vision, pages 591-600, December 1988.
[5] M. J. Black and A. Rangarajan . On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. Int'l J. of Computer Vision, 19: 57-91, 1996.
[6] K. J. Bowyer and P. J. Phillips (Eds.). Empirical evaluation techniques in computer vision. IEEE Computer Society, 1998.
[7] 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 Anal. Machine Intell., 16: 987-1001, 1994.
[8] H. Chen and P. Meer . Robust computer vision through kernel density estimation. In Proc. European Conf. on Computer Vision, volume I, pages 236-250, May 2002.
[9] H. Chen, P. Meer, and D. E. Tyler . Robust regression for data with multiple structures. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, volume I, pages 1069-1075, December 2001.
[10] Y. Cheng . Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Machine Intell., 17: 790-799, 1995.
[11] E. Choi and P. Hall . Data sharpening as a prelude to density estimation. Biometrika, 86: 941-947, 1999.
[12] W. Chojnacki, M. J. Brooks, A. van den Hengel, and D. Gawley . On the fitting of surfaces to data with covariances. IEEE Trans. Pattern Anal. Machine Intell., 22:1294-1303, 2000.
[13] C. M. Christoudias, B. Georgescu, and P. Meer . Synergism in low-level vision. In Proc. 16th International Conference on Pattern Recognition, volume IV, pages 150-155, August 2002.
[14] R. T. Collins . Mean-shift blob tracking through scale space. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, volume II, pages 234-240, 2003.
[15] D. Comaniciu . An algorithm for data-driven bandwidth selection. IEEE Trans. Pattern Anal. Machine Intell., 25: 281-288, 2003.
[16] D. Comaniciu and P. Meer . Distribution free decomposition of multivariate data. Pattern Analysis and Applications, 2: 22-30, 1999.
[17] D. Comaniciu and P. Meer . Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Machine Intell., 24: 603-619, 2002.
[18] D. Comaniciu, V. Ramesh, and P. Meer . The variable bandwidth mean shift and data-driven scale selection. In Proc. Int'l Conf. on Computer Vision, volume I, pages 438-445, July 2001.
[19] D. Comaniciu, V. Ramesh, and P. Meer . Kernel-based object tracking. IEEE Trans. Pattern Anal. Machine Intell., 25: 564-577, 2003.
[20] D. Donoho, I. Johnstone, P. Rousseeuw, and W. Stahel . Discussion: Projection pursuit. Annals of Statistics, 13: 496-500, 1985.
[21] R. O. Duda, P. E. Hart, and D. G. Stork . Pattern Classification, 2nd ed. Wiley, 2001.
[22] B. Efron and R. Tibshirani . An Introduction to the Bootstrap. Chapman & Hall, 1993.
[23] S. P. Ellis . Instability of least squares, least absolute deviation and least median of squares linear regression. Statistical Science, 13: 337-350, 1998.
[24] O. Faugeras . Three-Dimensional Computer Vision. MIT Press, 1993.
[25] M. A. Fischler and R. C. Bolles . Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. In DARPA Image Understanding Workshop, pages 71-88, University of Maryland, College Park, April 1980.
[26] M. A. Fischler and R. C. Bolles . Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. Assoc. Comp. Mach, 24(6): 381-395, 1981.
[27] A. W. Fitzgibbon, M. Pilu, and R. B. Fisher . Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Machine Intell., 21: 476-480, 1999.
[28] W. Förstner . Reliability analysis of parameter estimation in linear models with applications to mensuration problems in computer vision. Computer Vision, Graphics, and Image Processing, 40: 273-310, 1987.
[29] W. T. Freeman, E. G. Pasztor, and O. W. Carmichael . Learning in low-level vision. Int'l J. of Computer Vision, 40: 25-47, 2000.
[30] J. H. Friedman and J. W. Tukey . A projection pursuit algorithm for exploratory data analysis. IEEE Trans. Comput., 23: 881-889, 1974.
[31] K. Fukunaga . Introduction to Statistical Pattern Recognition, 2nd ed. Academic Press, 1990.
[32] K. Fukunaga and L. D. Hostetler . The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Information Theory, 21: 32-40, 1975.
[33] W. Fuller . Measurement Error Models. Wiley, 1987.
[34] B. Georgescu and P. Meer . Balanced recovery of 3D structure and camera motion from uncalibrated image sequences. In Proc. European Conf. on Computer Vision, volume II, pages 294-308, 2002.
[35] B. Georgescu, I. Shimshoni, and P. Meer . Mean shift based clustering in high dimensions: A texture classification example. In Proc. 9th Int'l Conf. on Computer Vision, pages 456-463, October 2003.
[36] E. B. Goldstein . Sensation and Perception, 2nd ed. Wadsworth, 1987.
[37] G. H. Golub and C. Reinsch . Singular value decomposition and least squares solutions. Number. Math., 14: 403-420, 1970.
[38] G. H. Golub and C. F. Van Loan . Matrix Computations, 2nd ed. Johns Hopkins U. Press, 1989.
[39] P. Hall, T. C. Hui, and J. S. Marron . Improved variable window kernel estimates of probability densities. Annals of Statistics, 23:1-10, 1995.
[40] R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel . Robust Statistics. The Approach Based on Influence Function. Wiley, 1986.
[41] R. M. Haralick and H. Joo . 2D-3D pose estimation. In Proceedings of the 9th International Conference on Pattern Recognition, pages 385-391, November 1988.
[42] R. M. Haralick and L. G. Shapiro . Computer and Robot Vision. AddisonWesley, 1992.
[43] R. Hartley and A. Zisserman . Multiple View Geometry in Computer Vision. Cambridge University Press, 2000.
[44] T. Hastie, R. Tibshirani, and J. Friedman . The Elements of Statistical Learning. Springer, 2001.
[45] T. P. Hettmansperger and S. J. Sheather . A cautionary note on the method of least median of squares. The American Statistician, 46: 79-83, 1992.
[46] P. V.C. Hough . Machine analysis of bubble chamber pictures. In International Conference on High Energy Accelerators and Instrumentation, Centre Européenne pour la Recherch Nucléaire (CERN), 1959.
[47] P. V.C. Hough . Method and means for recognizing complex patterns. US Patent 3,069,654, December 18, 1962.
[48] P. J. Huber . Projection pursuit (with discussion). Annals of Statistics, 13:435525, 1985.
[49] P. J. Huber . Robust Statistical Procedures, 2nd ed. SIAM, 1996.
[50] J. Illingworth and J. V. Kittler . A survey of the Hough transform. Computer Vision, Graphics, and Image Processing, 44: 87-116, 1988.
[51] M. Isard and A. Blake . Condensation-Conditional density propagation for visual tracking. Int'l J. of Computer Vision, 29: 5-28, 1998.
[52] A. K. Jain and R. C. Dubes . Algorithms for Clustering Data. Prentice Hall, 1988.
[53] J. M. Jolion, P. Meer, and S. Bataouche . Robust clustering with applications in computer vision. IEEE Trans. Pattern Anal. Machine Intell., 13: 791-802, 1991.
[54] M. C. Jones and R. Sibson . What is projection pursuit? (with discussion). J. Royal Stat. Soc. A, 150: 1-37, 1987.
[55] B. Julesz . Early vision and local attention. Rev. of Modern Physics, 63: 735-772, 1991.
[56] H. Kälviäinen, P. Hirvonen, L. Xu, and E. Oja . Probabilistic and nonprobabilistic Hough transforms: Overview and comparisons. Image and Vision Computing, 13: 239-252, 1995.
[57] K. Kanatani . Statistical bias of conic fitting and renormalization. IEEE Trans. Pattern Anal. Machine Intell., 16: 320-326, 1994.
[58] K. Kanatani . Statistical Optimization for Geometric Computation: Theory and Practice. Elsevier, 1996.
[59] D. Y. Kim, J. J. Kim, P. Meer, D. Mintz, and A. Rosenfeld . Robust computer vision: The least median of squares approach. In Proceedings 1989 DARPA Image Understanding Workshop, pages 1117-1134, May 1989.
[60] N. Kiryati and A. M. Bruckstein . What's in a set of points? IEEE Trans. Pattern Anal. Machine Intell., 14: 496-500, 1992.
[61] N. Kiryati and A. M. Bruckstein . Heteroscedastic Hough transform (HtHT): An efficient method for robust line fitting in the "errors in the variables" problem. Computer Vision and Image Understanding, 78: 69-83, 2000.
[62] V. F. Leavers . Survey: Which Hough transform? Computer Vision, Graphics, and Image Processing, 58: 250-264, 1993.
[63] K. M. Lee, P. Meer, and R. H. Park . Robust adaptive segmentation of range images. IEEE Trans. Pattern Anal. Machine Intell., 20: 200-205, 1998.
[64] Y. Leedan and P. Meer . Heteroscedastic regression in computer vision: Problems with bilinear constraint. Int'l J. of Computer Vision, 37: 127-150, 2000.
[65] A. Leonardis and H. Bischof . Robust recognition using eigenimages. Computer Vision and Image Understanding, 78: 99-118, 2000.
[66] R. M. Lewis, V. Torczon, and M. W. Trosset . Direct search methods: Then and now. J. Computational and Applied Math., 124: 191-207, 2000.
[67] G. Li. Robust regression . In D. C. Hoaglin, F. Mosteller, and J. W. Tukey (Eds.), Exploring Data Tables, Trends, and Shapes, pages 281-343. Wiley, 1985.
[68] R. A. Maronna and V. J. Yohai . The breakdown point of simultaneous general M estimates of regression and scale. J. of Amer. Stat. Assoc., 86: 699-703, 1991.
[69] R. D. Martin, V. J. Yohai, and R. H. Zamar . Min-max bias robust regression. Annals of Statistics, 17: 1608-1630, 1989.
[70] B. Matei . Heteroscedastic Errors-in-Variables Models in Computer Vision. PhD thesis, Department of Electrical and Computer Engineering, Rutgers University, 2001. Available at http://www.caip.rutgers.edu/riul/research/theses.html.
[71] B. Matei and P. Meer . Bootstrapping errors-in-variables models. In B. Triggs, A. Zisserman, and R. Szeliski (Eds.), Vision Algorithms: Theory and Practice, pages 236-252. Springer, 2000.
[72] B. Matei and P. Meer . Reduction of bias in maximum likelihood ellipse fitting. In Proc. Int'l Conf. on Pattern Recognition, volume III, pages 802-806, September 2000.
[73] P. Meer and B. Georgescu . Edge detection with embedded confidence. IEEE Trans. Pattern Anal. Machine Intell., 23: 1351-1365, 2001.
[74] P. Meer, D. Mintz, D. Y. Kim, and A. Rosenfeld . Robust regression methods in computer vision: A review. Int'l J. of Computer Vision, 6: 59-70, 1991.
[75] J. M. Mendel . Lessons in Estimation Theory for Signal Processing, Communications, and Control. Prentice Hall, 1995.
[76] J. V. Miller and C. V. Stewart . MUSE: Robust surface fitting using unbiased scale estimates. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 300-306, June 1996.
[77] D. Mintz, P. Meer, and A. Rosenfeld . Consensus by decomposition: A paradigm for fast high breakdown point robust estimation. In Proceedings 1991 DARPA Image Understanding Workshop, pages 345-362, January 1992.
[78] J. A. Nelder and R. Mead . A simplex method for function minimization. Computer Journal, 7: 308-313, 1965.
[79] P. L. Palmer, J. Kittler, and M. Petrou . An optimizing line finder using a Hough transform algorithm. Computer Vision and Image Understanding, 67: 1-23, 1997.
[80] D. Petkovic, W. Niblack, and M. Flickner . Projection-based high accuracy measurement of straight line edges. Machine Vision and Appl., 1: 183-199, 1988.
[81] P. D. Picton . Hough transform references. Internat. J. of Patt. Rec and Artific. Intell., 1: 413-425, 1987.
[82] W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery . Numerical Recipes in C, 2nd ed. Cambridge University Press, 1992.
[83] P. Pritchett and A. Zisserman . Wide baseline stereo matching. In Proc. Int'l Conf. on Computer Vision, pages 754-760, January 1998.
[84] Z. Pylyshyn . Is vision continuous with cognition? The case for cognitive impenetrability of visual perception. Behavioral and Brain Sciences, 22: 341-423, 1999. (with comments).
[85] S. J. Raudys and A. K. Jain . Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans. Pattern Anal. Machine Intell., 13: 252-264, 1991.
[86] P. J. Rousseeuw . Least median of squares regression. J. of Amer. Stat. Assoc., 79: 871-880, 1984.
[87] P. J. Rousseeuw . Unconventional features of positive-breakdown estimators. Statistics & Prob. Letters, 19: 417-431, 1994.
[88] P. J. Rousseeuw and C. Croux . Alternatives to the median absolute deviation. J. of Amer. Stat. Assoc., 88: 1273-1283, 1993.
[89] P. J. Rousseeuw and A. M. Leroy . Robust Regression and Outlier Detection. Wiley, 1987.
[90] D. Ruppert and D. G. Simpson . Comment on "Unmasking Multivariate Outliers and Leverage Points," by P. J. Rousseeuw and B. C. van Zomeren . J. of Amer. Stat. Assoc., 85: 644-646, 1990.
[91] D. W. Scott . Parametric statistical modeling by minimum integrated square error. Technometrics, 43: 247-285, 2001.
[92] B. W. Silverman . Density Estimation for Statistics and Data Analysis. Chapman & Hall, 1986.
[93] Special Issue. Performance evaluation. Machine Vision and Appl., 9(5/6), 1997.
[94] Special Issue. Robust statistical techniques in image understanding. Computer Vision and Image Understanding, 78, April 2000.
[95] G. Speyer and M. Werman . Parameter estimates for a pencil of lines: Bounds and estimators. In Proc. European Conf. on Computer Vision, volume I, pages 432-446,2002.
[96] L. Stark and K. W. Bowyer . Achieving generalized object recognition through reasoning about association of function to structure. IEEE Trans. Pattern Anal. Machine Intell., 13: 1097-1104, 1991.
[97] C. V. Stewart . Bias in robust estimation caused by discontinuities and multiple structures. IEEE Trans. Pattern Anal. Machine Intell., 19: 818-833, 1997.
[98] C. V. Stewart . Robust parameter estimation in computer vision. SIAM Reviews, 41: 513-537, 1999.
[99] C. V. Stewart . Minpran: A new robust estimator for computer vision. IEEE Trans. Pattern Anal. Machine Intell., 17: 925-938, 1995.
[100] K. S. Tatsuoka and D. E. Tyler . On the uniqueness of S and constrained M-functionals under non-elliptical distributions. Annals of Statistics, 28: 1219-1243, 2000.
[101] P. R. Thrift and S. M. Dunn . Approximating point-set images by line segments using a variation of the Hough transform. Computer Vision, Graphics, and Image Processing, 21: 383-394, 1983.
[102] A. Tirumalai and B. G. Schunk . Robust surface approximation using least median of squares. Technical Report CSE-TR-13-89, Artificial Intelligence Laboratory, 1988. University of Michigan, Ann Arbor.
[103] B. Tordoff and D. W. Murray . Guided sampling and consensus for motion estimation. In Proc. European Conf. on Computer Vision, volume I, pages 82-96, May 2002.
[104] P. H.S. Torr and C. Davidson . IMPSAC: Synthesis of importance sampling and random sample consensus. IEEE Trans. Pattern Anal. Machine Intell., 25: 354-364, 2003.
[105] P. H.S. Torr and D. W. Murray . The development and comparison of robust methods for estimating the fundamental matrix. Int'l J. of Computer Vision, 24: 271-300, 1997.
[106] P. H.S. Torr and A. Zisserman . MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 78: 138-156, 2000.
[107] P. H.S. Torr, A. Zisserman, and S. J. Maybank . Robust detection of degenerate configurations while estimating the fundamental matrix. Computer Vision and Image Understanding, 71: 312-333, 1998.
[108] P. H.S. Torr and A. Zisserman . Robust computation and parametrization of multiple view relations. In Proc. Int'l Conf. on Computer Vision, pages 727-732, January 1998.
[109] A. Treisman . Features and objects in visual processing. Scientific American, 254: 114-125, 1986.
[110] E. Trucco and A. Verri . Introductory Techniques for 3-D Computer Vision. Prentice Hall, 1998.
[111] S. Van Huffel and J. Vandewalle . The Total Least Squares Problem. Computational Aspects and Analysis. Society for Industrial and Applied Mathematics, 1991.
[112] M. P. Wand and M. C. Jones . Kernel Smoothing. Chapman & Hall, 1995.
[113] I. Weiss . Line fitting in a noisy image. IEEE Trans. Pattern Anal. Machine Intell., 11: 325-329, 1989.
[114] M. H. Wright . Direct search methods: Once scorned, now respectable. In D. H. Griffiths and G. A. Watson (Eds.), Numerical Analysis 1995, pages 191-208. Addison-Wesley Longman, 1996.
[115] R. H. Zamar . Robust estimation in the errors-in-variables model. Biometrika, 76: 149-160, 1989.
[116] Z. Zhang . Determining the epipolar geometry and its uncertainty: A review. Int'l J. of Computer Vision, 27: 161-195, 1998.
[117] Z. Zhang . Parameter-estimation techniques: A tutorial with application to conic fitting. Image and Vision Computing, 15: 59-76, 1997.