In this paper we show how Weighted Cone-Curvature (WCC) Models are suitable to carry out clustering tasks. CC is a new feature extracted from mesh models that gives an extended geometrical surroundings knowledge for every node of the mesh. WCC concept reduces the dimensionality of the object model without loss of information. A similarity measure based on the WCC feature has been defined and implemented to compare 3D objects using their models. Thus a similarity matrix based on WCC corresponding to an object database is the input of a fuzzy c-means algorithm to carry out an optimal partition of it. This algorithm divides the object database into disjoints clusters, objects in the same cluster being somehow more similar than objects in different clusters. The method has been experimentally tested in our lab under real conditions and the main results are shown in this work.


Similarity Matrix Modeling Wave Mesh Model Object Database Cluster Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Miguel Adán
    • 1
  • Antonio Adán
    • 2
  • Andrés S. Vázquez
    • 2
  1. 1.Departamento de Matemática AplicadaUCLMCiudad RealSpain
  2. 2.Departamento de Ingeniería Eléctrica, Electrónica y AutomáticaUCLMCiudad RealSpain

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