A general method for eliminating the bias of nonlinear estimators using bootstrap is presented. Instead of the traditional mean bias we consider the definition of bias based on the median. The method is applied to the problem of fitting ellipse segments to noisy data. No assumption beyond being independent identically distributed (i.i.d.) is made about the error distribution and experiments with both synthetic and real data prove the effectiveness of the technique.

Original languageEnglish (US)
Pages (from-to)752-756
Number of pages5
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number7
StatePublished - 1996

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


  • Bootstrap
  • Curve fitting
  • Implicit models
  • Low-level processing


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