Learning 2D Hand Shapes Using the Topology Preservation Model GNG

  • Anastassia Angelopoulou
  • José García Rodríguez
  • Alexandra Psarrou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3951)


Recovering the shape of a class of objects requires establishing correct correspondences between manually or automatically annotated landmark points. 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. To measure the quality of the mapping throughout the adaptation process we use the topographic product. Results are given for the training set of hand outlines.


Input Space Input Pattern Minimum Description Length Active Contour Model Statistical Shape Model 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Anastassia Angelopoulou
    • 1
  • José García Rodríguez
    • 2
  • Alexandra Psarrou
    • 1
  1. 1.Harrow School of Computer ScienceUniversity of WestminsterHarrowUnited Kingdom
  2. 2.Departamento de Tecnología Informática y ComputaciónUniversidad de AlicanteAlicanteSpain

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