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3D Posture Representation Using Meshless Parameterization with Cylindrical Virtual Boundary

  • Yunli Lee
  • Keechul Jung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4872)

Abstract

3D data is getting popular which offers more details and accurate information for posture recognition. However, it leads to computational hurdles and is not suitable for real time application. Therefore, we introduce a dimension reduction method using meshless parameterization with cylindrical virtual boundary for 3D posture representation. The meshless parameterization is based on convex combination approach which has good properties, such as fast computation and one-to-one mapping characteristic. This method depends on the number of boundary points. However, 3D posture reconstruction using silhouettes extraction from multiple cameras had resulted various number of boundary points. Therefore, a cylindrical virtual boundary points is introduced to overcome the inconsistency of 3D reconstruction boundary points. The proposed method generates five slices of 2D parametric appearance to represent a 3D posture for recognition purpose.

Keywords

3D voxel dimension reduction meshless parameterization posture recognition cylindrical virtual boundary 

References

  1. 1.
    Lee, Y., Kyoung, D., Han, E., Jung, K.: Dimension Reduction in 3D Gesture Recognition Using Meshless Parameterization. In: Chang, L.-W., Lie, W.-N., Chiang, R. (eds.) PSIVT 2006. LNCS, vol. 4319, pp. 64–73. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Floater, M.S.: Meshless Parameterization and B-spline Surface Approximation. In: Cipolla, R., Martin, R. (eds.) The Mathematics of Surfaces IX, pp. 1–18. Springer, Heidelberg (2000)Google Scholar
  3. 3.
    Van Floater, M.S., Reimers, M.: Meshless Parameterization and Surface Reconstruction. Computer Aided Geometric Design, 77–92 (2001)Google Scholar
  4. 4.
    Floater, M.S., Hormann, K.: Surface Parameterization: a Tutorial and Survey. Advances in Multiresolution for Geometric Modelling, 157–186 (2004)Google Scholar
  5. 5.
    Ye, G., Corso, J.J., Hager, G.D.: Gesture Recognition Using 3D Appearance and Motion Features. In: Proceeding IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
  6. 6.
    Shin, H.-K., Lee, S.-W., Lee, S.-W.: Real-Time Gesture Recognition Using 3D Motion History Model. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 888–898. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Malassiotis, S., Aifanti, N., Strintzis, M.G.: A Gesture Recognition System Using 3D Data. In: Proceedings of the First International Symposium on 3D Data Processing Visualization and Transmission, pp. 190–193 (2002)Google Scholar
  8. 8.
    Huang, T.S., Pavlovic, V.I.: Hand Gesture Modeling, Analysis, and Synthesis. Int. Workshop on Automatic Face-and Gesture-Recognition, Zurich, pp. 26–28 (1995)Google Scholar
  9. 9.
    Chu, C.-W., Cohen, I.: Posture and Gesture Recognition using 3D Body Shapes Decomposition. IEEE Workshop on Vision for Human-Computer Interaction  (2005)Google Scholar
  10. 10.
    Morrison, K., McKenna, S.J.: An Experimental Comparison of Trajectory-Based and History-Based Representation for Gesture Recognition. In: Camurri, A., Volpe, G. (eds.) GW 2003. LNCS (LNAI), vol. 2915, pp. 152–163. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Weiland, D., Ronfard, R., Boyer, E.: Motion History Volumes for Free Viewpoint Action Recognition. IEEE International Workshop on modeling People and Human Interaction PHI 2005  (2005)Google Scholar
  12. 12.
    Sato, Y., Saito, M., Koike, H.: Real-time Input of 3D Pose and Gestures of a User’s Hand and Its Applications for HCI. In: Proceeding IEEE Virtual Reality Conference, pp. 79–86. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  13. 13.
    Wu, Y., Huang, T.S.: Vision-Based Gesture Recognition: A Review. In: Braffort, A., Gibet, S., Teil, D., Gherbi, R., Richardson, J. (eds.) GW 1999. LNCS (LNAI), vol. 1739, Springer, Heidelberg (2000)Google Scholar
  14. 14.
    Teng, X., Wu, B., Yu, W., Liu, C.: A Hand Gesture Recognition System based on Local Linear Embedding. Journal of Visual Languages & Computing  (2005)Google Scholar
  15. 15.
    Dong, Q., Wu, Y., Hu, Z.: Gesture Recognition Using Quadratic Curves. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3851, pp. 817–825. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Mori, G., Ren, X., Efros, A.A., Malik, J.: Recovering Human Body Configurations: Combining Segmentation and Recognition. In: CVRP 2004, Washington, DC, vol. 2, pp. 326–333 (2004)Google Scholar
  17. 17.
    de Silva, V., Tenenbaum, J.B.: Global versus Local Methods in Nonlinear Dimensionality Reduction. Advances in Neural Information Processing Systems  (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yunli Lee
    • 1
  • Keechul Jung
    • 1
  1. 1.School of Media, College of Information Technology, Soongsil University, SeoulSouth Korea

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