Summary
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Growing Neural Gas (GNG): A Soft Competitive Learning Method for 2D Hand Modelling Jose GARCIA RODRIGUEZ Anastassia ANGELOPOULOU Alexandra PSARROU Publication IEICE TRANSACTIONS on Information and Systems Vol.E89-D No.7 pp.2124-2131 Publication Date: 2006/07/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e89-d.7.2124 Print ISSN: 0916-8532 Type of Manuscript: Special Section PAPER (Special Section on Machine Vision Applications) Category: Shape Models Keyword: image segmentation, classification, growing neural gas, registration, Full Text: PDF(874KB)>>
Summary: 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 study, we utilise a novel approach to automatically recover the shape of hand outlines from a series of 2D training images. Automated landmark extraction is accomplished through the use of the self-organising model the growing neural gas (GNG) network, which is able to learn and preserve the topological relations of a given set of input patterns without requiring a priori knowledge of the structure of the input space. The GNG is compared to other self-organising networks such as Kohonen and Neural Gas (NG) maps and results are given for the training set of hand outlines, showing that the proposed method preserves accurate models. | ||||||||||
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