D Hand Modelling" /> D 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." /> D Hand Modelling" />
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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,  

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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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