A new method for automatically building statistical\ud shape models from a set of training examples and in\ud particular from a class of hands. In this method, landmark\ud extraction is achieved using a self-organising neural\ud network, the Growing Neural Gas (GNG), which is used\ud to preserve the topology of any input space. Using GNG,\ud the topological relations of a given set of deformable\ud shapes can be learned. We describe how shape models can\ud be built automatically by posing the correspondence\ud problem on the behaviour of self-organising networks that\ud are capable of adapting their topology to an input\ud manifold, and due to their dynamic character to readapt it\ud to the shape of the objects. Results are given for the\ud training set of hand outlines, showing that the proposed\ud method preserves accurate models
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