To read this content please select one of the options below:

Body shape classification and block optimization based on space vector length

Jie Sun (School of Fashion Design and Engineering, Zhejiang Sci-Tech University, Hangzhou, China) (School of Design and Art, Communication University of Zhejiang, Hangzhou, China)
Qianyun Cai (School of Fashion Design and Engineering, Zhejiang Sci-Tech University, Hangzhou, China)
Tao Li (School of Fashion Design and Engineering, Zhejiang Sci-Tech University, Hangzhou, China)
Lei Du (School of Fashion Design and Engineering, Zhejiang Sci-Tech University, Hangzhou, China) (Zhejiang Provincial Research Center of Clothing Engineering Technology, Zhejiang Sci-Tech University, Hangzhou, China)
Fengyuan Zou (School of Fashion Design and Engineering, Zhejiang Sci-Tech University, Hangzhou, China) (Zhejiang Provincial Research Center of Clothing Engineering Technology, Zhejiang Sci-Tech University, Hangzhou, China)

International Journal of Clothing Science and Technology

ISSN: 0955-6222

Article publication date: 4 December 2018

Issue publication date: 18 February 2019

288

Abstract

Purpose

Considering two-dimensional features in the body shape classification system cannot fully reflect the three-dimensional (3D) morphological characteristics of human body. The purpose of this paper is to propose a 3D feature based method to characterize and classify the upper body shape of women, and then obtained the corresponding garment block and improved the fitness of clothing.

Design/methodology/approach

In this study, the [TC]2 3D scanner was used to obtain human data, and 15 layers of cross-sections of young females’ upper body were extracted. In total, 240 space vectors were obtained with the center of the bust cross-section as the original point. By using the principal component analysis and K-means clustering analysis, the body shape classification based on the space vectors length was realized. The garment block corresponding to three body types was obtained using the 3D scanning data and the cross-section convex hull, and compared with existing garment block and evaluated fitness of the blocks.

Findings

In total, 11 main components used to characterize the 3D morphological features of young women were obtained, which could explain 95.28 percent features of young women’s upper body. By cluster analysis, the body shape of women was divided into three categories. The block of three body types was obtained by the construction of the convex hull model.

Originality/value

This paper investigates a classification method of the body shape based on space vector length, which can effectively reflect the difference of surface shape of human body and further improve the matching degree of human body and clothing.

Keywords

Acknowledgements

This study was financially supported by the National Natural Science Foundation of China (NO. 11671009) and 2011 Collaborative Innovation Center of Garment Personalized Customization of Zhejiang Province.

Citation

Sun, J., Cai, Q., Li, T., Du, L. and Zou, F. (2019), "Body shape classification and block optimization based on space vector length", International Journal of Clothing Science and Technology, Vol. 31 No. 1, pp. 115-129. https://doi.org/10.1108/IJCST-07-2018-0089

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

Related articles