Outlier Elimination for Robust Ellipse and Ellipsoid Fitting

TL;DR

This paper introduces a two-stage outlier elimination algorithm that enhances the robustness of ellipse and ellipsoid fitting by combining proximity-based detection with model-based methods, effectively handling various outlier types.

Contribution

The paper presents a novel two-stage outlier removal approach that integrates graph Laplacian-based detection with RANSAC-like methods for improved robustness in shape fitting.

Findings

Significant improvement in robustness demonstrated through simulations.

Effective elimination of various outlier types.

Reduced computational complexity compared to existing methods.

Abstract

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.