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Finding Articulated Body in Time-Series Volume Data

  • Tomoyuki Mukasa
  • Shohei Nobuhara
  • Atsuto Maki
  • Takashi Matsuyama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4069)

Abstract

This paper presents a new scheme for acquiring 3D kinematic structure and motion from time-series volume data, in particular, focusing on human body. Our basic strategy is to first represent the shape structure of the target in each frame by using aMRG, augmented Multiresolution Reeb Graph [6], and then deform each of the shape structures so that all of them can be identified as a common kinematic structure throughout the input frames. Although the shape structures can be very different from frame to frame, we propose to derive a unique kinematic structure by way of clustering some nodes of graph, based on the fact that they are partly coherent. The only assumption we make is that human body can be approximated by an articulated body with certain number of end-points and branches. We demonstrate the efficacy of the proposed scheme through some experiments.

Keywords

Initial Model Geodesic Distance Shape Structure Joint Point Kinematic Structure 
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.

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References

  1. 1.
    Laurentini, A.: How Far 3D Shapes Can Be Understood from 2D Silhouettes. IEEE Trans. Pattern Analysis and Machine Intelligence 17(2), 188–195 (1995)CrossRefGoogle Scholar
  2. 2.
    Burr, D.J.: A Dynamic Model for Image Registration. Computer Graphics and Image Processing 15, 102–112 (1981)CrossRefGoogle Scholar
  3. 3.
    Nobuhara, S., Matsuyama, T.: Heterogenieous Deformation Model for 3D Shape and Motion Recovery from Multi-Viewpoint Images. In: 2nd International Symposium on 3D Data Processing, Visualization, and Transmission, pp. 566–573 (2004)Google Scholar
  4. 4.
    Masaaki, I., Yoshinari, K., Michihiko, M.: Estimation of the Location of Joint Points of Human Body from Successive Volume Data. In: ICPR 2000, pp. 699–702 (2002)Google Scholar
  5. 5.
    Hilaga, M., Shinagawa, Y., Kohmura, T., Kunii, T.L.: Topology Matching for Fully Automatic Similarity Estimation of 3D Shapes. In: Proc. of SIGGRAPH 2001, pp. 203–212 (2001)Google Scholar
  6. 6.
    Tung, T., et al.: The augmented multiresolution Reeb graph approach for content-based retrieval of 3D shapes. International Journal of Shape Modeling (IJSM) 11(1), 91–120 (2005)CrossRefGoogle Scholar
  7. 7.
    Kenmochi, Y., Kotani, K., Imiya, A.: Marching Cubes Method with Connectivity. In: Proc. of International Conference on Image Processing, pp. 361–365 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tomoyuki Mukasa
    • 1
  • Shohei Nobuhara
    • 1
  • Atsuto Maki
    • 1
  • Takashi Matsuyama
    • 1
  1. 1.Graduate School of InfomaticsKyoto UniversityJapan

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