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Two-Stage Outlier Elimination for Robust Curve and Surface Fitting

Abstract

An outlier elimination algorithm for curve/surface fitting is proposed. This two-stage hybrid algorithm employs a proximity-based outlier detection algorithm, followed by a model-based one. First, a proximity graph is generated. Depending on the use of a hard/soft threshold of the connectivity of observations, two algorithms are developed, one graph-component-based and the other eigenspace-based. Second, a model-based algorithm, taking the classification of inliers/outliers of the first stage as its initial state, iteratively refits and retests the observations with respect to the curve/surface model until convergence. These two stages compensate for each other so that outliers of various types can be eliminated with a reasonable amount of computation. Compared to other algorithms, this hybrid algorithm considerably improves the robustness of ellipse/ellipsoid fitting for scenarios with large portions of outliers and high levels of inlier noise, as demonstrated by extensive simulations.

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Correspondence to Jieqi Yu.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Yu, J., Zheng, H., Kulkarni, S.R. et al. Two-Stage Outlier Elimination for Robust Curve and Surface Fitting. EURASIP J. Adv. Signal Process. 2010, 154891 (2010). https://doi.org/10.1155/2010/154891

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Keywords

  • Information Technology
  • Quantum Information
  • Detection Algorithm
  • Hybrid Algorithm
  • Outlier Detection