Nonparametric modelling and tracking with Active-GNG

Angelopoulou, Anastassia and Psarrou, Alexandra and Gupta, Gaurav and Garcia Rodriguez, Jose (2007) Nonparametric modelling and tracking with Active-GNG. In: Human-Computer Interaction: IEEE international workshop, HCI 2007, Rio de Janeiro, Brazil, October 20, 2007; proceedings. Lecture notes in computer science (4796). Springer, Berlin, pp. 98-107. ISBN 9783540757726

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Official URL: http://dx.doi.org/10.1007/978-3-540-75773-3_11

Abstract

In this paper we address the correspondence problem, with its application to nonrigid tracking and unsupervised modelling, as a nonparametric, active-linking topology learning problem. Unlike existing soft competitive learning methods, Active Growing Neural Gas (A-GNG) has both global and local properties which allows part of the network to reconfigure while tracking. In addition, A-GNG uses a number of features (e.g. topographic product, local grey-level and map transformation) so that the topological relations are preserved and nodes correspondences are retained between tracked configurations. Experimental results in a sequence of hand gestures and artificial data have shown the superiority of our proposed method over the original GNG.

Item Type: Book Section
Subjects: University of Westminster > Science and Technology > Electronics and Computer Science, School of (No longer in use)
Depositing User: Miss Nina Watts
Date Deposited: 20 Jan 2009 15:50
Last Modified: 14 Oct 2009 11:37
URI: http://westminsterresearch.wmin.ac.uk/id/eprint/5658

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