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Robust Adaptive Segmentation of Range Images
February 1998 (vol. 20 no. 2)
pp. 200-205

Abstract—We propose a novel image segmentation technique using the robust, adaptive least kth order squares (ALKS) estimator which minimizes the kth order statistics of the squared of residuals. The optimal value of k is determined from the data, and the procedure detects the homogeneous surface patch representing the relative majority of the pixels. The ALKS shows a better tolerance to structured outliers than other recently proposed similar techniques: Minimize the Probability of Randomness (MINPRAN) and Residual Consensus (RESC). The performance of the new, fully autonomous, range image segmentation algorithm is compared to several other methods.

[1] F. Arman and J.K. Aggrawal, "Model-Based Object Recognition in Dense-RangeImages—A Review," ACM Computing Surveys, vol. 25, no. 1, pp. 5-43, Mar. 1993.
[2] P.J. Besl, Surfaces in Range Image Understanding.New York: Springer-Verlag, 1988.
[3] P.J. Besl, J.B. Birch, and L.T. Watson, "Robust Window Operators," Machine Vision and Applications, vol. 2, pp. 179-192, 1989.
[4] 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, no. 10, pp. 987-1,001, Oct. 1994.
[5] T.J. Fan, G. Medioni, and R. Nevatia, "Segmented Descriptions of 3-D Surfaces," IEEE Trans. Robotics and Automation, vol. 3, no. 6, pp. 527-538, Dec. 1987.
[6] R.C. Bolles and M.A. Fischler, "A RANSAC-Based Approach to Model Fitting andIts Application to Finding Cylinders in Range Data," Proc. Sixth Int'l Joint Conf. Artificial Intelligence, pp. 637-643,Vancouver, Canada, Aug. 1981.
[7] R.M. Haralick and L.G. Shapiro, Computer and Robot Vision.Reading, Mass.: Addison-Wesley, 1992.
[8] A. Hoover, G. Jean-Baptiste, X. Jiang, P.J. Flynn, H. Bunke, D. Goldgof, K. Bowyer, D. Eggert, A. Fitzgibbon, and R. Fisher, “An Experimental Comparison of Range Segmentation Algorithms,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol 18, no. 7, pp. 673-689, July 1996.
[9] J. Jolion,P. Meer,, and S. Bataouche,“Robust clustering with applications in computer vision,” IEEE Trans. Pattern Analysis amd Machine Intelligence, vol. 13, no. 8, pp. 791-801, Aug. 1991.
[10] V. Koivunen and M. Pietikäinen, "Evaluating Quality of Surface Description Using Robust Methods," Proc. 11th Int'l Conf. Pattern Recognition, vol. 3, pp. 214-218,The Hague, The Netherlands, Aug. 1992.
[11] A. Leonardis, A. Gupta, and R. Bajcsy, "Segmentation of Range Images as the Search for Geometric Parametric Models," Int'l J. Computer Vision, vol. 14, no. 3, pp. 253-277, Apr. 1995.
[12] P. Meer, D. Mintz, D.Y. Kim, and A. Rosenfeld, "Robust Regression Methods for Computer Vision: A Review," Int'l J. Computer Vision, vol. 6, no. 1 pp. 59-70, Apr. 1991.
[13] P. Meer, D. Mintz, and A. Rosenfeld, "Least Median of Squares Based Robust Analysis of Image Structure," Proc. 1990 DARPA Image Understanding Workshop, pp. 231-254,Pittsburgh, Pa., Sept. 1990.
[14] J.V. Miller and C.V. Stewart, "MUSE: Robust Surface Fitting Using Unbiased Scale Estimates," Proc. Computer Vision and Pattern Recognition '96, pp. 300-306,San Francisco, June 1996.
[15] D. Mintz, P. Meer, and A. Rosenfeld, "Consensus by Decomposition: A Paradigm for Fast High Breakdown Point Robust Estimation," Proc. 1991 DARPA Image Understanding Workshop, pp. 345-362,La Jolla, Calif., Jan. 1992.
[16] M. Rioux, "Laser Range Finder Based on Synchronized Scanners," Applied Opt., vol. 23, no. 21, pp. 3,837-3,844, 1984.
[17] P.J. Rousseeuw and A.M. Leroy, Robust Regression and Outlier Detection.New York: John Wiley&Sons, 1987.
[18] P.J. Rousseeuw and C. Croux, "Alternatives to the Median Absolute Deviations," J. Am. Statistical Assoc., vol. 88, pp. 1,273-1,283, Dec. 1993.
[19] G. Roth and M.D. Levine, "Extracting Geometric Primitives," CVGIP: Image Understanding, vol. 58, no. 1, pp. 1-22, July 1993.
[20] C.V. Stewart, "Expected Performance of Robust Estimators Near Discontinuities," Proc. Fifth Int'l Conf. Computer Vision, pp. 969-974,Boston, Mass., June 1995.
[21] C.V. Stewart, “MINPRAN: A New Robust Estimator for Computer Vision,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 10, pp. 925-938, Oct. 1995.
[22] X. Yu, T.D. Bui, and A. Krzyzak, "Robust Estimation for Range Image Segmentation and Reconstruction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 5, pp. 530-538, May 1994.

Index Terms:
Robust methods, least kth order squares, range image segmentation, surface fitting, autonomous image analysis.
Citation:
Kil-Moo Lee, Peter Meer, Rae-Hong Park, "Robust Adaptive Segmentation of Range Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 2, pp. 200-205, Feb. 1998, doi:10.1109/34.659940
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