Wavelet moments for recognizing human body posture from 3D scans

Author

Werghi, Naoufel ; Xiao, Yijun

Author_Institution

Dept. of Comput. Sci., Glasgow Univ., UK

Volume

1

fYear

2002

fDate

11-15 Aug. 2002

Firstpage

319

Abstract

This paper addresses the problem of recognizing a human body (HB) posture from a cloud of 3D points acquired by a human body scanner It suggests the wavelet transform coefficients (WTC) as 3D shape descriptors of the HB posture. The WTC showed to have a high discrimination power between posture classes. Integrated within a Bayesian classification framework and compared with other standard moments, the WTC showed great capabilities in discriminating between close postures. The qualities of the WTC features were also reflected on its classification rate, ranked first when compared with other 3D features.

Keywords

Bayes methods; image classification; wavelet transforms; 3D point cloud; 3D scans; 3D shape descriptors; Bayesian classification framework; WTC; discrimination power; human body posture recognition; posture classes; wavelet moments; wavelet transform coefficients; Avatars; Bayesian methods; Biomedical imaging; Humans; Motion pictures; Shape; Three-dimensional displays; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2002. Proceedings. 16th International Conference on

Conference_Location

Quebec City, Quebec, Canada

ISSN

1051-4651

Print_ISBN

0-7695-1695-X

Type

conf

DOI

10.1109/ICPR.2002.1044704

Filename

1044704