Robust Adaptive Segmentation of Range Images

Kil-Moo Lee(1), Peter Meer(2) and Rae-Hong Park(1)

(1)Department of Electronic Engineering, Sogang University
C.P.O. Box 1142, Seoul 100-611, Korea

(2)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08855, USA

Robust high breakdown point estimators are now routinely employed in range image segmentation algorithms. We propose a novel technique using the adaptive least k-th order square (ALKS) estimator which minimizes the k-th order statistics of the squared of residuals. The optimal value of k is determined from the data, and the procedure does not require that the pixels of the homogeneous surface patch are in absolute majority in the window of analysis. The ALKS shows a better tolerance to structured outliers than other recently proposed similar techniques: MINPRAN and RESC. The performance of the new, fully autonomous, range image segmentation algorithm compares favorably to other methods.

This is an extended version of the correspondence published in, IEEE Trans. Pattern Anal. Machine Intell. vol. 20, 200-205, 1998.
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