In many computer vision problems, it is necessary to robustly estimate parameter values from a large quantity of image data. In such problems, least-squares minimization is computationally the most convenient and practical solution method. The author shows that the least-squares solution is in general statistically biased in the presence of noise. A scheme called renormalization that iteratively removes the statistical bias by automatically adjusting to the image noise is presented. It is applied to the problem of estimating vanishing points and focuses of expansion and conic fitting.
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