Direct aspect-based 3-D object recognition

  • Massimiliano Pontil
  • Alessandro Verri
Poster Session C: Compression, Hardware & Software, Image Databases, Neural Networks, Object Recognition & Reconstruction
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)


In this paper a method for 3-D object recognition based on Support Vector Machines (SVM) is proposed. Given a set of points which belong to either of two classes, a SVM finds the hyperplane that leaves the largest possible fraction of points of the same class on the same side, while maximizing the distance of the closest point. Recognition with SVMs does not require feature extraction and can be performed directly on images regarded as points of an N-dimensional object space. The potential of the proposed method is illustrated on a database of 7200 images of 100 different objects. The excellent recognition rates achieved in all the performed experiments indicate that the method is well-suited for aspect-based recognition.


  1. 31.
    Bazaraa, M. and Shetty, C.M. Nonlinear programming (John Wiley, New York, 1979).Google Scholar
  2. 2.
    Brunelli, R. and Poggio, T. 1993. “Face Recognition: Features versus Templates,” IEEE Trans. on PAMI 15: 1042–1052.Google Scholar
  3. 3.
    Cortes, C. and Vapnik V. 1995. “Support Vector Network,” Machine learning 20: 1–25.Google Scholar
  4. 4.
    Edelman, S., Bulthoff, H., and Weinshall, D. 1989. “Stimulus Familiarity Determines Recognition Strategy for Novel 3-D Objects,” AI Memo No. 1138, AI Lab, MIT.Google Scholar
  5. 5.
    Huttenlocher, D.P., Klanderman, G.A., and Rucklidge, W.J. 1993. “Comparing Images Using the Hausdorff Distance,” IEEE Trans. on PAMI 15:850–863.Google Scholar
  6. 6.
    Murase, H. and Nayar, S.K. 1995. “Visual Learning and Recognition of 3-D Object from Appearance,” Int. J. Comput. Vision 14:5–24.CrossRefGoogle Scholar
  7. 7.
    Osuna, E., Freund, R.,and Girosi, F. “Training Support Vector Machines: an Applications to Face Detection.” (Submitted to CVPR97).Google Scholar
  8. 8.
    Poggio, T. and Edelman, S. 1990. “A Network that Learns to Recognize Three Dimensional Objects,” Nature, Vol. 343, pp. 263–266.CrossRefPubMedGoogle Scholar
  9. 9.
    Tarr, M. and Pinker, S. 1989. “Mental Rotation and Orientation-Dependence in Shape Recognition,” Cognitive Psychology, Vol. 21, pp. 233–282.CrossRefPubMedGoogle Scholar
  10. 10.
    Vapnik, V. The Nature of Statistical Learning Theory (Springer-Verlag, New York, 1995).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Massimiliano Pontil
    • 1
  • Alessandro Verri
    • 1
  1. 1.INFM - Dipartimento di Fisica dell'Università di GenovaGenova (I)

Personalised recommendations