3D human posture segmentation by spectral clustering with surface normal constraint

Publication Type:
Journal Article
Citation:
Signal Processing, 2011, 91 (9), pp. 2204 - 2212
Issue Date:
2011-09-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2010004626.pdf1.75 MB
Adobe PDF
In this paper, we propose a new algorithm for partitioning human posture represented by 3D point clouds sampled from the surface of human body. The algorithm is formed as a constrained extension of the recently developed segmentation method, spectral clustering (SC). Two folds of merits are offered by the algorithm: (1) as a nonlinear method, it is able to deal with the situation that data (point cloud) are sampled from a manifold (the surface of human body) rather than the embedded entire 3D space; (2) by using constraints, it facilitates the integration of multiple similarities for human posture partitioning, and it also helps to reduce the limitations of spectral clustering. We show that the constrained spectral clustering (CSC) still can be solved by generalized eigen-decomposition. Experimental results confirm the effectiveness of the proposed algorithm. © 2011 Elsevier B.V. All rights reserved.
Please use this identifier to cite or link to this item: