Abstract
Introducing a mathematical model of image noise, we formalize the problem of fitting a conic to point data as statistical estimation. It is shown that the reliability of the fitted conic can be evaluated quantitatively in the form of the covariance tensor. We present a numerical scheme called renormalization for computing an optimal fit and at the same time evaluating its reliability. We also present a scheme for visualizing the reliability of the fit by means of the primary deviation pair. Our method is illustrated by showing simulations and real-image examples.