원문정보
보안공학연구지원센터(IJMUE)
International Journal of Multimedia and Ubiquitous Engineering
Vol.9 No.8
2014.08
pp.213-220
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
With the emergence of numerous 3D human motion capture databases, the effective analysis and handling of human motion data have become a major challenge so that the use of motion capture databases can be maximized. To reduce the high-dimensional complexity of data, a type of geometrical feature based on 2D geometrical space law is first extracted from human motion for the application of motion data into a low-dimensional subspace. With the aim of achieving a low-dimensional feature, identification and classification in different motions are then conducted through spectral clustering based on manifold learning to realize the automatic identification and retrieval of 3D human motion.
목차
Abstract
1. Introduction
2. The Module of Feature Selection and Extraction
3. Spectral Clustering
3.1. Manifold Learning Spectral Clustering (MLSC)
3.2. The Main Steps of ISOMAP Algorithm
4. The Experimental Result and Analysis
Acknowledgements
References
1. Introduction
2. The Module of Feature Selection and Extraction
3. Spectral Clustering
3.1. Manifold Learning Spectral Clustering (MLSC)
3.2. The Main Steps of ISOMAP Algorithm
4. The Experimental Result and Analysis
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보