Free-Shaped Object Recognition Method from Partial Views Using Weighted Cone Curvatures

  • Santiago Salamanca
  • Carlos Cerrada
  • Antonio Adán
  • Jose A. Cerrada
  • Miguel Adán
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

This work presents a method for free-shaped object recognition from its partial views. Consecutive database reductions are achieved in three stages by using effective discriminant features. These features are extracted from the spherical mesh representation used to modeling the partial view and from the view range data itself. The used characteristics are global, which means that they can not represent the views univocally. However, their staged application allows the initial object database to be reduced to selecting just one candidate in the final stage with a high success rate. Yet, the most powerful search reduction is achieved in the first stage where the new Weighted Cone Curvature (WCC) parameter is processed. The work is devoted to describe the overall method making especial emphasis in the WCC feature and its application to partial views recognition. Results with real objects range data are also presented in the paper.

Keywords

Wave Front Machine Intelligence Range Data Partial Model Iterative Close Point 
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.
    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
  2. 2.
    Adán, A., Cerrada, C., Feliu, V.: Modeling wave set: Definition and application of a new topological organization of 3D object modeling. Computer Vision and Image Understanding 79(2), 281–307 (2000)CrossRefGoogle Scholar
  3. 3.
    Adán, A., Cerrada, C., Feliu, V.: Global shape invariants: a solution for 3D free-form object discrimination/identification problem. Pattern Recognition 34(7), 1331–1348 (2001)MATHCrossRefGoogle Scholar
  4. 4.
    Campbell, R.J., Flynn, P.J.: Eigenshapes for 3D object recognition in range data. In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, June 1999, vol. 2, pp. 2505–2510 (1999)Google Scholar
  5. 5.
    Hebert, M., Ikeuchi, K., Delingette, H.: A spherical representation for recognition of free-form surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(7), 681–690 (1995)CrossRefGoogle Scholar
  6. 6.
    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(5), 433–449 (1999)CrossRefGoogle Scholar
  7. 7.
    Skocaj, D., Leonardis, A.: Robust recognition and pose determination of 3-D objects using range image in eigenspace approach. In: Proc. of 3DIM 2001, pp. 171–178 (2001)Google Scholar
  8. 8.
    Stein, F., Medioni, G.: Structural indexing: Efficient 3-D object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 125–145 (1992)CrossRefGoogle Scholar
  9. 9.
    Yamany, S., Farag, A.: Surfacing signatures: An orientation independent free-form surface representation scheme for the purpose of objects registration and matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8), 1105–1120 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Santiago Salamanca
    • 1
  • Carlos Cerrada
    • 2
  • Antonio Adán
    • 3
  • Jose A. Cerrada
    • 2
  • Miguel Adán
    • 4
  1. 1.Escuela de Ingenierías IndustrialesUniversidad de ExtremaduraBadajozSpain
  2. 2.Escuela Técnica Superior de Ingeniería InformáticaMadridSpain
  3. 3.Escuela Superior de InformáticaUniversidad de Castilla La ManchaCiudad RealSpain
  4. 4.Escuela de Ingeniería Técnica AgrícolaUniversidad de Castilla la ManchaCiudad RealSpain

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