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Nonparametric Modelling and Tracking with Active-GNG.
- Source :
- Human:Computer Interaction; 2007, p98-107, 10p
- Publication Year :
- 2007
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540757726
- Database :
- Complementary Index
- Journal :
- Human:Computer Interaction
- Publication Type :
- Book
- Accession number :
- 33082993
- Full Text :
- https://doi.org/10.1007/978-3-540-75773-3_11