Advertisement

Abstract

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.

Keywords

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.

References

  1. 1.
    Sengupta, K., Boyer, K.L.: Creating Random Structural Descriptions of CAD Models and Determining Object Classes. In: Proc. IEEE Workshop on CAD-based Vision, pp. 38–45 (1994)Google Scholar
  2. 2.
    Sengupta, K., Boyer, K.L.: Modelbase Partitioning Using Property Matrix Spectra. Computer Vision and Image Understanding 70(2), 177–196 (1998)zbMATHCrossRefGoogle Scholar
  3. 3.
    Selinger, A., Nelson, R.: A perceptual Gruping Hierarchy for Appearance-Based 3D Object Recognition. Computer Vision and Image Understanding 76(1), 83–92 (1999)CrossRefGoogle Scholar
  4. 4.
    Froimovich, G., Rivlin, E., Shimshoni, I.: Object Classification by Functional Parts. In: Fisrt Symposium on 3D Data, Proccesing, Viasualization and Trnasmision. Padova, pp. 648–655 (2002)Google Scholar
  5. 5.
    Yeung, D.S., Wang, X.Z.: Improving Performance of Similarity-Based Clustering by Feature Weight Learning. IEEE Trans. On Pattern Analysis and Machine Intelligence 24(4), 556–561Google Scholar
  6. 6.
    Gdalyahu, Y., Weinshall, D.: Flexible Syntactic Matching of Curves and Its Application to Automatic Hierarchical Classification of Silhouettes. IEEE Trans. On Pattern Analysis and Machine Intelligence 21(12), 1312–1328 (1999)CrossRefGoogle Scholar
  7. 7.
    Cyr, C., Kimia, M.B.B.: 3D Object recognition using shape similarity-based aspect graph. In: Int. Conference on Computer Vision, Vancouver. pp. 254–261 (2001)Google Scholar
  8. 8.
    Ohbuchi, R., Minamitani, T., Takei, T.: Shape-Similarity search of 3D Models by Using Enhanced Shape Functions. In: Proc of the Theory and Practice of Computer Graphics, TPCG 2003 (2003)Google Scholar
  9. 9.
    Osada, R., Funkhouser, T., Chazelle, D., Dobkin, D.: Matching 3D Models with Shapes Distributions. In: Proc. Int. Conf. On Shape Modeling and Applications, Genova, pp. 154–166 (2001)Google Scholar
  10. 10.
    Adán, M., Adán, A.: Solids Characterization Using Modeling Wave Structures. In: Perales, F.J., Campilho, A.C., Pérez, N., Sanfeliu, A. (eds.) IbPRIA 2003. LNCS, vol. 2652, pp. 1–10. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Adán, M., Adán, A., Cerrada, C., Merchán, P., Salamanca, S.: Weighted Cone-Curvature: Applications to 3D Similarity Shapes. In: The Fourth International Conference on 3-D Digital Imaging and Modeling (3D DIM), Banff., pp 458–465 (2003)Google Scholar
  12. 12.
    Adán, A., Cerrada, C., Feliú, V.: Modeling Wave Set: Definition and Application of a new Topological Organization for 3D Object Modeling. Computer Vision and Image Understanding 79, 281–307 (2000)CrossRefGoogle Scholar
  13. 13.
    Jolliffe, I.T.: Principle Components Analysis. Springer, New York (1986)Google Scholar
  14. 14.
    Duda, R.O., Hart, P.E.: Pattern Classification and Scene Análisis. Jonh Wiley & Sons, Chichester (1973)Google Scholar
  15. 15.
    Bezdek, J.C., Pal, S.K.: Fuzzy Models For Pattern Recognition. IEEE Press, Los Alamitos (1992)Google Scholar

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

Personalised recommendations