CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 1998 vol.20 Issue No.02 - February
Issue No.02 - February (1998 vol.20)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.659940
<p><b>Abstract</b>—We propose a novel image segmentation technique using the robust, adaptive least <it>k</it>th order squares (ALKS) estimator which minimizes the <it>k</it>th order statistics of the squared of residuals. The optimal value of <it>k</it> 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.</p>
Robust methods, least kth order squares, range image segmentation, surface fitting, autonomous image analysis.
Kil-Moo Lee, Peter Meer, Rae-Hong Park, "Robust Adaptive Segmentation of Range Images", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.20, no. 2, pp. 200-205, February 1998, doi:10.1109/34.659940