A growing neural gas algorithm with applications in hand modelling and tracking

Authors:
Anastassia Angelopoulou;Alexandra Psarrou;José García Rodríguez
Affiliations:
School of Electronics and Computer Science, University of Westminster, UK;School of Electronics and Computer Science, University of Westminster, UK;Department of Computing Technology, University of Alicante, Spain
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Year:
2011

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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.