About this Journal Submit a Manuscript Table of Contents
Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 613939, 29 pages
http://dx.doi.org/10.1155/2012/613939
Research Article

Mixed Signature: An Invariant Descriptor for 3D Motion Trajectory Perception and Recognition

1Department of MEEM, City University of Hong Kong, Hong Kong
2Department of PMPI, University of Science and Technology of China, Hefei 230027, China
3Department of MEEM, USTC-CityU Joint Advanced Research Centre, Suzhou 215123, China
4Department of CS, University of Science and Technology of China, Hefei 230027, China
5Department of Mathematics, University of Salerno, Via Ponte Don Melillo, 84084 Fisciano (SA), Italy
6Department of Mathematics, Sapienza University of Rome, P.le A. Moro 2, 00185 Rome, Italy

Received 30 March 2011; Accepted 27 April 2011

Academic Editor: Shengyong Chen

Copyright © 2012 Jianyu Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Psarrou, S. Gong, and M. Walter, “Recognition of human gestures and behaviour based on motion trajectories,” Image and Vision Computing, vol. 20, no. 5-6, pp. 349–358, 2002. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Billard and R. Siegwart, “Robot learning from demonstration,” Robotics and Autonomous Systems, vol. 47, no. 2-3, pp. 65–67, 2004. View at Publisher · View at Google Scholar · View at Scopus
  3. K. Vloker, L. H. Dennis, B. Sanmohan, U. Ales, and K. Danica, “Learning actions from observations,” IEEE Robotics and Automation Magazine, vol. 17, no. 2, pp. 30–43, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. K. K. Lee, M. Yu, and Y. Xu, “Modeling of human walking trajectories for surveillance,” in IEEE International Conference on Intelligent Robots and Systems, vol. 2, pp. 1554–1559, 2003.
  5. J. Martin, D. Hall, and J. L. Crowley, “Statistical gesture recognition through modeling of parameter trajectories,” Lecture Notes in Computer Science, vol. 1739, pp. 129–140, 1999.
  6. E. Bribiesca, “A chain code for representing 3D curves,” Pattern Recognition, vol. 33, no. 5, pp. 755–765, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Berretti, A. D. Bimbo, and P. Pala, “Retrieval by shape similarity with perceptual distance and effective indexing,” IEEE Transactions on Multimedia, vol. 2, no. 4, pp. 225–239, 2000. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Masood, “Optimized polygonal approximation by dominant point deletion,” Pattern Recognition, vol. 41, no. 1, pp. 227–239, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  9. V. V. Kindratenko, “On using functions to describe the shape,” Journal of Mathematical Imaging and Vision, vol. 18, no. 33, pp. 225–245, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  10. M. J. Black and A. D. Jepson, “A probabilistic framework for matching temporal trajectories: condensation-based recognition of gestures and expressions,” in Proceedings of the European Conference on Computer Vision, vol. 1, pp. 909–924, Freiburg, Germany, 1998.
  11. F. S. Cohen, Z. Huang, and Z. Yang, “Invariant matching and identification of curves using B-splines curve representation,” IEEE Transactions on Image Processing, vol. 4, no. 1, pp. 1–10, 1995. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  12. S. Y. Chen and Q. Guan, “Parametric shape representation by a deformable NURBS model for cardiac functional measurements,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 3, pp. 480–487, 2011. View at Publisher · View at Google Scholar · View at PubMed
  13. M. C. Shin, L. V. Tsap, and D. B. Goldgof, “Gesture recognition using Bezier curves for visualization navigation from registered 3-D data,” Pattern Recognition, vol. 37, no. 5, pp. 1011–1024, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. P. R. G. Harding and T. J. Ellis, “Recognizing hand gesture using Fourier descriptors,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR '04), pp. 286–289, August 2004. View at Scopus
  15. G. C. H. Chuang and C. C. J. Kuo, “Wavelet descriptor of planar curves: theory and applications,” IEEE Transactions on Image Processing, vol. 5, no. 1, pp. 56–70, 1996. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  16. C. Cattani, “Shannon wavelets for the solution of integrodifferential equations,” Mathematical Problems in Engineering, vol. 2010, Article ID 408418, 22 pages, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  17. S. Tabbone, L. Wendling, and J. P. Salmon, “A new shape descriptor defined on the Radon transform,” Computer Vision and Image Understanding, vol. 102, no. 1, pp. 42–51, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. I. Weiss, “Geometric invariants and object recognition,” International Journal of Computer Vision, vol. 10, no. 3, pp. 207–231, 1993. View at Publisher · View at Google Scholar · View at Scopus
  19. D. Forsyth, J. L. Mundy, A. Zisserman, and C. M. Brown, “Projectively invariant representations using implicit algebraic curves,” Image and Vision Computing, vol. 9, no. 2, pp. 130–136, 1991. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Manay, D. Cremers, B. W. Hong, A. J. Yezzi, and S. Soatto, “Integral invariants for shape matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1602–1617, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  21. S. Y. Chen, J. Zhang, Q. Guan, and S. Liu, “Detection and amendment of shape distortions based on moment invariants for active shape models,” IET Image Processing, vol. 5, no. 3, pp. 273–285, 2011.
  22. Y. Caspi, D. Simakov, and M. Irani, “Feature-based sequence-to-sequence matching,” International Journal of Computer Vision, vol. 68, no. 1, pp. 53–64, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. S. Y. Chen, H. Tong, Z. Wang, S. Liu, M. Li, and B. Zhang, “Improved generalized belief propagation for vision processing,” Mathematical Problems in Engineering, vol. 2011, Article ID 416963, 12 pages, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  24. M. Scalia, G. Mattioli, and C. Cattani, “Analysis of large-amplitude pulses in short time intervals: application to neuron interactions,” Mathematical Problems in Engineering, vol. 2010, Article ID 895785, 15 pages, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  25. M. Li, W. Zhao, and S. Chen, “MBm-based scalings of traffic propagated in internet,” Mathematical Problems in Engineering, vol. 2011, Article ID 389803, p. 21, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  26. K. Ogawara, Y. Tanabe, R. Kurazume, and T. Hasegawa, “Detecting repeated motion patterns via dynamic programming using motion density,” in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1743–1749, 2009. View at Publisher · View at Google Scholar
  27. A. Prati, S. Calderara, and R. Cucchiara, “Using circular statistics for trajectory shape analysis,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), June 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. D. R. Faria and J. Dias, “3D hand trajectory segmentation by curvatures and hand orientation for classification through a probabilistic approach,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '09), pp. 1284–1289, December 2009. View at Publisher · View at Google Scholar
  29. E. Ribnick and N. Papanikolopoulos, “View-invariant analysis of periodic motion,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '09), pp. 1903–1908, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. J. Sun, X. Wu, S. Yan, L. -F. Cheong, T. -S. Chua, and J. Li, “Hierarchical spatio-temporal context modeling for action recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR '09), pp. 2004–2011, 2009. View at Publisher · View at Google Scholar
  31. S. D. Wu and Y. F. Li, “Flexible signature descriptions for adaptive motion trajectory representation, perception and recognition,” Pattern Recognition, vol. 42, no. 1, pp. 194–214, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  32. J. Y. Yang, Y. F. Li, and K. Y. Wang, “Mixed signature descriptor with global invariants for 3D motion trajectory perception and recognition,” in Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1952–1956, 2010.
  33. S. Wu and Y. F. Li, “On signature invariants for effective motion trajectory recognition,” International Journal of Robotics Research, vol. 27, no. 8, pp. 895–917, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. UCI KDD ASL Archive, http://kdd.ics.uci.edu/databases/auslan2/auslan.html.
  35. E. Calabi, P. J. Olver, C. Shakiban, A. Tannenbaum, and S. Haker, “Differential and numerically invariant signature curves applied to object recognition,” International Journal of Computer Vision, vol. 26, no. 2, pp. 107–135, 1998. View at Publisher · View at Google Scholar · View at Scopus
  36. L. R. Rabiner and B. H. Huang, Fundamentals of Speech Recognition, Prentice Hall, 1993.
  37. M. Munich and P. Perona, “Continuous dynamic time warping for translation-invariant curve alignment with applications to signature verification,” in Proceedings of the IEEE International Conference on Computer Vision, vol. 1, pp. 108–115, 1999.
  38. R. E. Kalman, “A new approach to linear filtering and prediction problems,” Transaction of ASME, Journal of Basic Engineering, vol. 82, pp. 35–45, 1960.
  39. S. Chen, J. Zhang, H. Zhang et al., “Myocardial motion analysis for determination of tei-index of human heart,” Sensors, vol. 10, no. 12, pp. 11428–11439, 2010. View at Publisher · View at Google Scholar