Topological segmentation of discrete human body shapes in various postures based on geodesic distance

Yijun, X., Siebert, J.P. and Werghi, N. (2004) Topological segmentation of discrete human body shapes in various postures based on geodesic distance. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, pp. 131-135. ISBN 0769521282 (doi:10.1109/ICPR.2004.1334486)

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Publisher's URL: http://dx.doi.org/10.1109/ICPR.2004.1334486

Abstract

his paper extends our previous Reeb graph approach based on a new Morse function, namely geodesic distance, to segment whole body scan data into primary body parts in various postures. Because of the bending invariance of geodesic distance, the resulting Reeb graph can remain stable in a large range of postures. Consequently, the approach is capable of segmenting data within the posture range. The application of geodesic distance also brings the independence of coordinate frame selection. We present a number of experiments conducted on both real body 3D scan samples and simulated datasets to demonstrate the validity of the approach.

Item Type:Book Sections
Status:Published
Glasgow Author(s) Enlighten ID:Siebert, Dr Jan
Authors: Yijun, X., Siebert, J.P., and Werghi, N.
College/School:College of Science and Engineering > School of Computing Science
Publisher:IEEE
ISBN:0769521282

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