Robust adaptive segmentation of range images
- 1 February 1998
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 20 (2) , 200-205
- https://doi.org/10.1109/34.659940
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 squares 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. The performance of the new, fully autonomous, range image segmentation algorithm is compared to several other methods.Keywords
This publication has 18 references indexed in Scilit:
- MUSE: robust surface fitting using unbiased scale estimatesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996
- Segmentation of range images as the search for geometric parametric modelsInternational Journal of Computer Vision, 1995
- The Robust Sequential Estimator: a general approach and its application to surface organization in range dataPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1994
- Alternatives to the Median Absolute DeviationJournal of the American Statistical Association, 1993
- Model-based object recognition in dense-range images—a reviewACM Computing Surveys, 1993
- Robust regression methods for computer vision: A reviewInternational Journal of Computer Vision, 1991
- Robust window operatorsMachine Vision and Applications, 1989
- Segmented descriptions of 3-D surfacesIEEE Journal on Robotics and Automation, 1987
- Robust Regression and Outlier DetectionPublished by Wiley ,1987
- Laser range finder based on synchronized scannersApplied Optics, 1984