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Robust Estimation for Range Image Segmentation and Reconstruction
May 1994 (vol. 16 no. 5)
pp. 530-538

This correspondence presents a segmentation and fitting method using a new robust estimation technique. We present a robust estimation method with high breakdown point which can tolerate more than 80% of outliers. The method randomly samples appropriate range image points in the current processing region and solves equations determined by these points for parameters of selected primitive type. From K samples, we choose one set of sample points that determines a best-fit equation for the largest homogeneous surface patch in the region. This choice is made by measuring a residual consensus (RESC), using a compressed histogram method which is effective at various noise levels. After we get the best-fit surface parameters, the surface patch can be segmented from the region and the process is repeated until no pixel left. The method segments the range image into planar and quadratic surfaces. The RESC method is a substantial improvement over the least median squares method by using histogram approach to inferring residual consensus. A genetic algorithm is also incorporated to accelerate the random search.

[1] D. H. Ballard, "Generalizing Hough transform to detect arbitrary shapes,"Pattern Recognit., vol. 13, pp. 111-122, 1981.
[2] P. J. Besl,Surfaces in Range Image Understanding. Berlin: Springer-Verlag, 1988.
[3] P. J. Besl and R. C. Jain, "Segmentation through variable-order surface fitting,"IEEE Trans. Pattern Anal. Machine Intell., vol. 10, no. 2, pp. 167-192, Feb. 1988.
[4] L. Davis, "Adapting operator probabilities in genetic algorithms," inProc. 3rd Int. Conf. on Genetic Algorithm, 1989, pp. 61-69.
[5] R.O. Duda and P.E. Hart, "Use of the Hough transformation to detect lines and curves in pictures,"Commun. Ass. Comput. Mach., vol. 15, no. 1, pp. 11-15, Jan. 1972.
[6] T.-J. Fan, G. Medioni, and R. Nevatia, "Segmented description of 3-D surfaces,"IEEE J. Robotics Automat., Dec. 1987, pp. 527-538.
[7] O. D. Faugeras and M. Hebert, "The representation, recognition, and positioning of 3-D shapes from range data," inTechniques for 3-D Machine Perception. New York: Elsevier Science, 1986, pp. 13-51.
[8] M. A. Fischler and R. C. Bolles, "Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,"Commun. ACM, vol. 24, no. 6, pp. 381-395, 1981.
[9] P. J. Flynn, "CAD-based computer vision: Modeling and recognition strategies," Ph.D. dissertation, Dep. Comput. Sci., Michigan State Univ., 1990.
[10] R. Hoffman and A. K. Jain, "Segmentation and classification of range images,"IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI-9, pp. 608-619, 1987.
[11] J. Illingworth and J. Kittler, "A survey of the Hough transform,"Comput. Vision Graphics Image Processing, vol. 44, pp. 87-116, 1988.
[12] A. K. Jain and S. G. Nadabar, "MRF model-based segmentation of range images," inProc. of 3rd Int. Conf. on Comput. Vision, 1990, pp. 667-671.
[13] J.-M. Jolion, P. Meer, and S. Bataouche. "Robust clustering with applications in computer vision,"IEEE Trans. Pattern Anal. Machine Intell., vol. 13, no. 8, pp. 791-802, Aug. 1991.
[14] B. Kamgar-Parsi, B. Kamgar-Parsi, and N. S. Netanyahu, "A nonparametric method for fitting a straight line to a noisy image,"IEEE Trans. Pattern Anal. Machine Intell., vol. 11, no. 9, pp. 998-1001, 1989.
[15] T. Kasvand, "Extraction of edges in 3-D range images to subpixel," inProc. 9th Int. Conf. on Pattern Recognit., 1988, pp. 93-98.
[16] P. Meer, D. Mintz, A. Rosenfeld, and D. Kim, "Robust regression methods for computer vision: A review,"Int. J. Comput. Vision, vol. 6, pp. 59-70, Apr. 1991.
[17] G. Roth and M. D. Levine, "Segmentation of geometric signals using robust fitting," inProc. 10th Int. Conf. on Pattern Recognit., vol. 1, pp. 826-831, 1990.
[18] G. Roth and M. D. Levine, "A genetic algorithm for primitive extraction," inProc. 4th Int. Conf. on Genetic Algorithms, 1991, pp. 487-494.
[19] P. J. Rousseeuw and A. M. Leroy,Robust Regression&Outlier Detection. New York: Wiley, 1987.
[20] B. Sahata, F. Arman, and J. K. Aggarwal, "Segmentation of 3-D range images using pyramidal data structures," inProc. 3rd Int. Conf. on Comput. Vision, 1990, pp. 662-666.
[21] P. T. Sander and S. W. Zucker, "Tracing surfaces for surfacing traces," inProc. 1st Int. Conf. on Comput. Vision, 1987, pp. 241-249.
[22] G. Syswerda, "Uniform crossover in genetic algorithms," inProc. 3rd Int. Conf. on Genetic Algorithms, 1989, pp. 2-9.
[23] G. Taubin, "Estimation of planar curves, surfaces, and nonplanar space curves defined by implicit equations with applications to edge and range image segmentation,"IEEE Pattern Anal. Machine Intell., vol. 13, no. 11, pp. 1115-1138, Nov. 1991.
[24] L. Xu, E. Oja, and P. Kultanen, "A new curve detection method: Randomized Hough transform (RHT),"Patt. Recognition Lett., vol. 11, pp. 331-338, 1990.
[25] N. Yokoya and M. D. Levine, "Range image segmentation based on differential geometry: A hybrid approach,"Pattern Anal. Machine Intell., vol. 11, no. 6, p. 643, June 1989.
[26] X. M. Yu, T. D. Bui, and A. Krzyzak, "Invariants and pose determination," inVISUAL FORM: Analysis and Recognition, Proc. of the Int. Workshop on Visual Form. New York: Plenum, 1992, pp. 623-632.
[27] X. M. Yu, T. D. Bui, and A. Krzyzak, "The genetic algorithm parameter settings for robust estimation and range image seg. and fitting," inProc. 8th Scandinavian Conf. on Image Anal., 1993, pp. 623-630.

Index Terms:
image segmentation; image reconstruction; genetic algorithms; robust estimation; range image segmentation; image reconstruction; primitive parameters; homogeneous surface patch; residual consensus; RESC; compressed histogram method; best-fit surface parameters; planar surfaces; quadratic surfaces; least median squares method; genetic algorithm; random search
Citation:
X. Yu, T.D. Bui, A. Krzyzak, "Robust Estimation for Range Image Segmentation and Reconstruction," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 5, pp. 530-538, May 1994, doi:10.1109/34.291443
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