Two-stage outlier elimination for robust curve and surface fitting

Jieqi Yu, Haipeng Zheng, Sanjeev R. Kulkarni, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Article number154891
JournalEurasip Journal on Advances in Signal Processing
Volume2010
DOIs
StatePublished - 2010

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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