Extracting Features from Point Set Model

Author

Pang, Xu-Fang ; Pang, Ming-Yong

Author_Institution

Dept. of Educ. Technol., Nanjing Normal Univ., Nanjing, China

Volume

1

fYear

2009

fDate

10-11 Oct. 2009

Firstpage

595

Lastpage

599

Abstract

This paper present an method for feature extraction from point set. Our algorithm use principle curvatures to flag potential feature points. Using an improved weight sensitive moving least squares, we developed a new approach to detect potential feature curves. The potential feature points are enhanced by projecting the points onto the local potential feature curves. Then smooth the projected points by employing an optimized principal covariance analysis approach. Finally achieve smooth feature curves after resolving gaps and relaxing the results.

Keywords

computational geometry; covariance analysis; curve fitting; feature extraction; least squares approximations; optimisation; curvature principle; feature extraction; improved weight sensitive moving least square approach; optimized principal covariance analysis; point set model; Clouds; Computer vision; Educational technology; Feature extraction; Least squares approximation; Least squares methods; Multilevel systems; Polynomials; Robustness; Surface fitting; MLS; feature enhancement; feature extraction; point set;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on

Conference_Location

Changsha, Hunan

Print_ISBN

978-0-7695-3804-4

Type

conf

DOI

10.1109/ICICTA.2009.150

Filename

5287580