| Chapter title | Nonparametric modelling and tracking with Active-GNG |
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| Authors | Angelopoulou, A., Psarrou, A., Gupta, G. and Garcia Rodriguez, J. |
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| Editors | Lew, M., Sebe, N., Huang, T.S. and Bakker, E.M. |
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| 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. |
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| Book title | Human-Computer Interaction: IEEE international workshop, HCI 2007, Rio de Janeiro, Brazil, October 20, 2007; proceedings |
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| Year | 2007 |
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| Publisher | Springer |
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| Publication dates |
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| Published | 2007 |
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| Place of publication | Berlin |
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| Series | Lecture notes in computer science |
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| ISBN | 9783540757726 |
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| Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-540-75773-3_11 |
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| Journal citation | (4796), pp. 98-107 |
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