  In this paper, an outlier elimination algorithm for ellipse/ellipsoid fitting
is proposed. This two-stage algorithm employs a proximity-based outlier
detection algorithm (using the graph Laplacian), followed by a model-based
outlier detection algorithm similar to random sample consensus (RANSAC). These
two stages compensate for each other so that outliers of various types can be
eliminated with reasonable computation. The outlier elimination algorithm
considerably improves the robustness of ellipse/ellipsoid fitting as
demonstrated by simulations.
