Angelopoulou, Anastassia, Psarrou, Alexandra, 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 9783540757726Full text not available from this repository.
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|
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