A new numerical scheme is presented for strictly computing maximum likelihood (ML) of geometric fitting
problems. Intensively studied in the past are those methods that first transform the data into a computationally
convenient form and then assume Gaussian noise in the transformed space. In contrast, our method assumes
Gaussian noise in the original data space. It is shown that the strict ML solution can be computed by iteratively
using existing methods. Then, our method is applied to ellipse fitting and fundamental matrix computation.
Our method is also shown to encompasses optimal correction, computing, e.g., perpendiculars to an ellipse and
triangulating stereo images. While such applications have been studied individually, our method generalizes
them into an application independent form from a unified point of view.