Angelopoulou, Anastassia and Psarrou, Alexandra and Gupta, Gaurav and Garcia Rodriguez, Jose (2007) Robust modelling and tracking of NonRigid objects using Active-GNG. In: IEEE Workshop on Non-rigid Registration and Tracking through Learning, NRTL 2007, in conjunction with ICCV 2007, 14-21 October 2007, Rio de Janeiro. IEEE, Los Alamitos, USA, pp. 1-7. ISBN 9781424416301
Official URL: http://dx.doi.org/10.1109/ICCV.2007.4409179
This paper presents a robust approach to nonrigid modelling and tracking. The contour of the object is described by an active growing neural gas (A-GNG) network which allows the model to re-deform locally. The approach is novel in that the nodes of the network are described by their geometrical position, the underlying local feature structure of the image, and the distance vector between the modal image and any successive images. A second contribution is the correspondence of the nodes which is measured through the calculation of the topographic product, a topology preserving objective function which quantifies the neighbourhood preservation before and after the mapping. As a result, we can achieve the automatic modelling and tracking of objects without using any annotated training sets. Experimental results have shown the superiority of our proposed method over the original growing neural gas (GNG) network.
|Item Type:||Book Section|
|Research Community:||University of Westminster > Electronics and Computer Science, School of|
|Deposited On:||20 Jan 2009 15:39|
|Last Modified:||11 Aug 2010 15:34|
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