Analysis of the least median of squares estimator for computer vision applications
- 2 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 621-623
- https://doi.org/10.1109/cvpr.1992.223126
Abstract
The robust least-median-of-squares (LMedS) estimator, which can recover a model representing only half the data points, was recently introduced in computer vision. Image data, however, is usually also corrupted by a zero-mean random process (noise) accounting for the measurement uncertainties. It is shown that in the presence of significant noise, LMedS loses its high breakdown point property. A different, two-stage approach in which the uncertainty due to noise is reduced before applying the simplest LMedS procedure is proposed. The superior performance of the technique is proved by comparative graphs Author(s) Mintz, D. LSI Logic Corp., Milpitas, CA, USA Meer, P. ; Rosenfeld, AzrielKeywords
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