Angelopoulou, Anastassia and Garcia Rodriguez, Jose and Psarrou, Alexandra (2005) Automatic landmark extraction from a class of hands using growing neural gas. In: Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005. IAPR MVA Conference Committee, Tokyo, Japan, pp. 168-171. ISBN 4901122045
A new method for automatically building statistical shape models from a set of training examples and in particular from a class of hands. In this method, landmark extraction is achieved using a self-organising neural network, the Growing Neural Gas (GNG), which is used to preserve the topology of any input space. Using GNG, the topological relations of a given set of deformable shapes can be learned. We describe how shape models can be built automatically by posing the correspondence problem on the behaviour of self-organising networks that are capable of adapting their topology to an input manifold, and due to their dynamic character to readapt it to the shape of the objects. Results are given for the training set of hand outlines, showing that the proposed method preserves accurate models.
|Item Type:||Book Section|
|Research Community:||University of Westminster > Electronics and Computer Science, School of|
|Deposited On:||22 Sep 2005|
|Last Modified:||11 Aug 2010 15:29|
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