Estimation of curvature from sampled noisy data

Chang Kyu Lee, Robert M. Haralick, Koichiro Deguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)

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 languageEnglish
Title of host publicationIEEE Computer Vision and Pattern Recognition
Editors Anon
PublisherPubl by IEEE
Pages536-541
Number of pages6
ISBN (Print)0818638826
Publication statusPublished - 1993 Dec 1
EventProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - New York, NY, USA
Duration: 1993 Jun 151993 Jun 18

Publication series

NameIEEE 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

  • Engineering(all)

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  • Cite this

    Lee, C. K., Haralick, R. M., & Deguchi, K. (1993). Estimation of curvature from sampled noisy data. In Anon (Ed.), IEEE Computer Vision and Pattern Recognition (pp. 536-541). (IEEE Computer Vision and Pattern Recognition). Publ by IEEE.