Estimation of curvature from sampled noisy data

Chang Kyu Lee, Robert M. Haralick, Koichiro Deguchi

研究成果: Conference contribution

22 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルIEEE Computer Vision and Pattern Recognition
編集者 Anon
出版社Publ by IEEE
ページ536-541
ページ数6
ISBN(印刷版)0818638826
出版ステータスPublished - 1993 12 1
イベントProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - New York, NY, USA
継続期間: 1993 6 151993 6 18

出版物シリーズ

名前IEEE Computer Vision and Pattern Recognition

Other

OtherProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
CityNew York, NY, USA
Period93/6/1593/6/18

ASJC Scopus subject areas

  • 工学(全般)

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