Share Email Print
cover

Proceedings Paper

Understanding 3D human torso shape via manifold clustering
Author(s): Sheng Li; Peng Li; Yun Fu
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Discovering the variations in human torso shape plays a key role in many design-oriented applications, such as suit designing. With recent advances in 3D surface imaging technologies, people can obtain 3D human torso data that provide more information than traditional measurements. However, how to find different human shapes from 3D torso data is still an open problem. In this paper, we propose to use spectral clustering approach on torso manifold to address this problem. We first represent high-dimensional torso data in a low-dimensional space using manifold learning algorithm. Then the spectral clustering method is performed to get several disjoint clusters. Experimental results show that the clusters discovered by our approach can describe the discrepancies in both genders and human shapes, and our approach achieves better performance than the compared clustering method.

Paper Details

Date Published: 29 May 2013
PDF: 7 pages
Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 875013 (29 May 2013); doi: 10.1117/12.2018314
Show Author Affiliations
Sheng Li, Northeastern Univ. (United States)
Peng Li, U.S. Army Natick Soldier Research, Development and Engineering Ctr. (United States)
Yun Fu, Northeastern Univ. (United States)


Published in SPIE Proceedings Vol. 8750:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI
Harold H. Szu, Editor(s)

© SPIE. Terms of Use
Back to Top