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
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