Using Non Local Features for 3D Shape Grouping

  • Antonio Adán
  • Miguel Adán
  • Santiago Salamanca
  • Pilar Merchán
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5342)

Abstract

This work faces the problem of 3D shape clustering when the whole surface information is available. The key of our method is to use a flexible feature, called Cone-Curvature, which provides local and extended information around every node of the mesh that represents the object. Thus as we increase the region around a node a new order of CC can be calculated. This feature, which was originally defined on spherical representation, has been adapted to work with standard triangular meshes and it is used for defining a similarity measure between shapes. Through a PCA technique, the dimensionality of the shape representation is drastically reduced and the hierarchical grouping can be efficiently carried out. This method has been tested under real conditions for a wide set of free shapes yielding promising results. We present a discussion of the clustering comparing human and computer results.

Keywords

Object clustering Shape similarity 3D representation 3D models 

References

  1. 1.
    Santini, S., Jain, R.: Similarity measures. IEEE Trans. Pattern Anal. Mach. Intell 21(9), 871–883 (1999)CrossRefGoogle Scholar
  2. 2.
    Delinguette, H.: Simplex Meshes: A General Representation for 3D Shape Reconstruction, Technical Report 2214, INRIA, France (1994)Google Scholar
  3. 3.
    Koenderink, J.J., Van Doorn, A.J.: Surface shape and curvature scales. Image and Vision Computing 10(8), 557–564 (1992)CrossRefGoogle Scholar
  4. 4.
    Dyn, N., Hormann, K., Kim, S.J., Levin, D.: Optimizing 3D Triangulations Using Discrete Curvature Analysis. In: Mathematical Methods for Curves and Surfaces, pp. 135–146. Vanderbilt University Press (2000)Google Scholar
  5. 5.
    Alboul, L., Van Damme, R.: Polyhedral metrics in surface reconstruction. In: Mullineux, G. (ed.) The Mathematics of Surfaces VI, pp. 171–200. Clarendon Press, Oxford (1996)Google Scholar
  6. 6.
    Dorai, C., Jain, A.K.: COSMOS - A Representation Scheme for 3D Free-Form Objects. IEEE Trans. PAMI 19(10), 1115–1130 (1997)CrossRefGoogle Scholar
  7. 7.
    Johnson, A.E., Hebert, M.: Recognizing Objects by Matching Oriented Points. In: IEEE Conf. Comp. Vision and Pattern Recogn, Puerto Rico, June, pp. 684–689 (1997)Google Scholar
  8. 8.
    Yamany, S.M., Farag, A.A.: Free-Form Surface Registration Using Surface Signatures. In: Proc. IEEE Intern. Conf. on Computer Vision, vol. 2, pp. 1098–1104 (1999)Google Scholar
  9. 9.
    Yamany, S.M., Farag, A.A.: Surfacing Signatures, An Orientation Independent Free-Form Surface Representation Scheme for the Purpose of Objects Registration and Matching. IEEE Trans. Pattern Anal. Mach. Intell 24(8), 1105–1120 (2002)CrossRefGoogle Scholar
  10. 10.
    Chua, C.S., Jarvis, R.: Point Signatures, A New Representation for 3D Object Recognition. International Journal of Computer Vision 25(1), 63–85 (1997)CrossRefGoogle Scholar
  11. 11.
    Vandeborre, J.-P., Couillet, V., Daoudi, M.: A practical approach for 3D model indexing by combining local and global invariants. In: 1st Int. Symp. On 3D Data Processing Visualization and Transmission, Padova, Italy, pp. 644–647 (2002)Google Scholar
  12. 12.
    Adán, A., Cerrada, C., Feliu, V.: Global Shape Invariants: A Solution For 3D Object Discrimination/ Identification Problem. Pattern Recog. 34, 1331–1348 (2001)CrossRefMATHGoogle Scholar
  13. 13.
    Liu, X., Sun, R., Kang, S.B., Shum, H.Y.: Directional Histogram Model for Three-Dimensional Shape Similarity. In: CVPR 2003, vol. 1, pp. 813–820 (2003)Google Scholar
  14. 14.
    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
  15. 15.
    Adán, A., Adán, M.: A Flexible Similarity Measure for 3D Shapes Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(11), 1507–1520 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Antonio Adán
    • 1
  • Miguel Adán
    • 1
  • Santiago Salamanca
    • 2
  • Pilar Merchán
    • 2
  1. 1.Escuela Superior de InformáticaUniversidad de Castilla La ManchaCiudad RealSpain
  2. 2.Escuela de Ingenierías IndustrialesUniversidad de ExtremaduraBadajozSpain

Personalised recommendations