Growing Neural Gas (GNG): A Soft Competitive Learning Method for 2D Hand Modelling

  • Authors:

  • José GarcÍa RodrÍguez;Anastassia Angelopoulou;Alexandra Psarrou

  • Affiliations:

  • The author is with the Department of Computer Technology and Computation, University of Alicante, Alicante, Spain. E-mail: jgarcia@dtic.ua.es,;The authors are with the Department of Computer Science and Computer Vision Lab., University of Westminster, London, United Kingdom. E-mail: agelopa@wmin.ac.uk, E-mail: psarroa@wmin.ac.uk;The authors are with the Department of Computer Science and Computer Vision Lab., University of Westminster, London, United Kingdom. E-mail: agelopa@wmin.ac.uk, E-mail: psarroa@wmin.ac.uk

  • Venue:
  • IEICE - Transactions on Information and Systems
  • Year:
  • 2006

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Abstract

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.