### Abstract

Estimation of curvature from noisy sampled data is a fundamental problem in digital arc segmentation. The facet approach in curvature estimation involves least square fitting the observed data points to a parametric cubic polynomial and calculating the curvature analytically from the fitted parametric coefficients. Due to the fitting, there exists systematic error or bias between curvature calculated analytically from the parameterization of a circle and one calculated analytically based on the coefficients of the fitted cubic polynomial, even when the data is sampled from noiseless circle. We show how to compensate this bias by estimating it with the coefficients of the fitted cubic polynomial, which gives more accurate curvature value. We introduce small perturbations to the sampled data from a noiseless circle, and we analytically trace how the perturbation propagates through coefficients of the fitted polynomials and results in perturbation error of the curvature.

Original language | English |
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Title of host publication | IEEE Computer Vision and Pattern Recognition |

Editors | Anon |

Publisher | Publ by IEEE |

Pages | 536-541 |

Number of pages | 6 |

ISBN (Print) | 0818638826 |

Publication status | Published - 1993 Dec 1 |

Event | Proceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - New York, NY, USA Duration: 1993 Jun 15 → 1993 Jun 18 |

### Publication series

Name | IEEE Computer Vision and Pattern Recognition |
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### Other

Other | Proceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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City | New York, NY, USA |

Period | 93/6/15 → 93/6/18 |

### ASJC Scopus subject areas

- Engineering(all)

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## Cite this

*IEEE Computer Vision and Pattern Recognition*(pp. 536-541). (IEEE Computer Vision and Pattern Recognition). Publ by IEEE.