Angelopoulou, Anastassia and Psarrou, Alexandra and Gupta, Gaurav and Garcia Rodriguez, Jose (2007) Nonparametric modelling and tracking with Active-GNG. In: Lew, Michael and Sebe, Nicu and Huang, Thomas S. and Bakker, Erwin M., (eds.) 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
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|
|Research Community:||University of Westminster > Electronics and Computer Science, School of|
|Deposited On:||20 Jan 2009 15:50|
|Last Modified:||14 Oct 2009 12:37|
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