Abstract
Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced Growing Neural Gas (GNG) model for applications in hand modelling and tracking. The modified network consists of the geometric properties of the nodes, the underline local feature of the image, and an automatic criterion for maximum node growth based on the probability of the objects in the image. We present experimental results for hands and T1-weighted MRI images, and we measure topology preservation with the topographic product.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Angelopoulou, A., Psarrou, A., Gupta, G., García, J.: Robust Modelling and Tracking of Nonrigid Objects Using Active-GNG. In: IEEE Workshop on Non-rigid Registration and Tracking through Learning, NRTL 2007, in conjuction with ICCV 2007, pp. 1–7 (2007)
Bauer, H., Pawelzik, K.: Quantifying the neighbourhood preservation of self-organizing feature maps. IEEE Transactions on Neural Networks 3(4), 570–579 (1992)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active Shape Models - Their Training and Application. Comp. Vision Image Underst. 61(1), 38–59 (1995)
Cretu, A., Petriu, E., Payeur, P.: Evaluation of growing neural gas networks for selective 3D scanning. In: Proc. of IEEE International Workshop on Robotics and Sensors Environments, pp. 108–113 (2008)
Edwards, G., Taylor, C., Cootes, T.: Interpreting face images using active appearance models. In: Proc. of the International Conference on Face And Gesture Recognition, pp. 300–305 (1998)
Frezza-Buet, H.: Following non-stationary distributions by controlling the vector quantisation acccuracy of a growing neural gas network. Neurocomputing 71(7-9), 1191–1202 (2008)
Fritzke, B.: A growing Neural Gas Network Learns Topologies. In: Advances in Neural Information Processing Systems 7, NIPS 1994, pp. 625–632 (1995)
García-Rodríguez, J., Flórez-Revuelta, F., García-Chamízo, M.: Image Compression Using Growing Neural Gas. In: Proc. of the International Joint Conference on Artificial Neural Networks, pp. 366–370 (2007)
Hans-Joachim, B., Anja, B., Ulf-dietrich, B., Markus, K., Horst-michael, G.: Neural Networks for Gesture-based Remote Control of a Mobile Robot. In: Proceedings of the IEEE World Congress on Computational Intelligence, vol. 1, pp. 372–377 (1998)
Holdstein, Y., Fischer, A.: Three-dimensional surface reconstruction using meshing growing neural gas (MG NG). The Visual Computer: International Journal of Computer Graphics 24(4), 295–302 (2008)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. In: Proc. of the 1st Internationl Conference on Computer Vision, pp. 259–268. IEEE Computer Society Press, Los Alamitos (1987)
Lankton, S., Tannenbaum, A.: Localizing region-based active contours. IEEE Transactions on Image Processing 17(11), 2029–2039 (2008)
Qin, A.K., Suganthan, P.N.: Robust growing neural gas algorithm with application in cluster analysis. Neural Networks 17(8-9), 1135–1148 (2004)
Rivera-Rovelo, J., Herold, S., Bayro-Corrochano, E.: Object segmentation using growing neural gas and generalized gradient vector flow in the geometric algebra framework. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, pp. 306–315. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Angelopoulou, A., Psarrou, A., García Rodríguez, J. (2011). A Growing Neural Gas Algorithm with Applications in Hand Modelling and Tracking. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-21498-1_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21497-4
Online ISBN: 978-3-642-21498-1
eBook Packages: Computer ScienceComputer Science (R0)