Abstract
We describe the least median of squares (LMedS) robust estimator which identifies the surface corresponding to the absolute majority of the data points. However when all the data points are corrupted by noise LMedS may fail. This is the case in computer vision applications and we have developed a new approach which preserves the robustness of LMedS but avoids its artifacts in the presence of noise.
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