Continuous Hand Gesture Segmentation and Co-articulation Detection

  • M. K. Bhuyan
  • D. Ghosh
  • P. K. Bora
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


Gesture segmentation is an extremely difficult task due to both the multitude of possible gesture variations in spatio-temporal space and the co-articulation of successive gestures. In this paper, a robust framework for this problem is proposed which has been used to segment out component gestures from a continuous stream of gestures using finite state machine and motion features in a vision based platform.


Fuzzy Rule Gesture Recognition Global Motion Hand Gesture Recognition Negative Small 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Shamaie, A., Hai, W., Sutherland, A.: Hand gesture recognition for HCI. ERCIM News 46 (2001),
  2. 2.
    Zhao, L.: Synthesis and acquisition of laban movement analysis qualitative parameters for communicative gestures, Ph.D Thesis, CIS, University of Pennsylvania (2001)Google Scholar
  3. 3.
    Aggarwal, J., Cai, Q.: Human motion analysis: A review. In: Proc. Nonrigid and Articulated Motion Workshop, pp. 90–102 (1997)Google Scholar
  4. 4.
    Lee, H.K., Kim, J.H.: An HMM based threshold model approach for gesture recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 21(10), 961–973 (1999)CrossRefGoogle Scholar
  5. 5.
    Nishimura, T., Oka, R.: Towards the integration of spontaneous speech and gesture based on spotting method. In: Proc. IEEE/SICE/RSJ International Conf. Multisensor Fusion Integration Intelligent System, pp. 433–437 (1996)Google Scholar
  6. 6.
    Vogler, C., Mextaxas, D.: Adapting hidden Markov models for ASL recognition by using three-dimensional computer vision methods. In: Proc. IEEE International Conf. on Systems, Man and Cybernetics, pp. 156–161 (1997)Google Scholar
  7. 7.
    Bhuyan, M.K., Ghosh, D., Bora, P.K.: Key video object plane selection by MPEG-7 visual shape descriptor for summarization and recognition of hand gestures. In: Proc. 4th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), pp. 638–643 (2004)Google Scholar
  8. 8.
    Kendon, A.: Conducting interaction. Cambridge University Press, Cambridge (1990)Google Scholar
  9. 9.
    Huttenlocher, D.P., Noh, J.J., Rucklidge, W.J.: Tracking non-rigid objects in complex scene. In: Proc. 4th International Conf. Computer Vision, pp. 93–101 (1993)Google Scholar
  10. 10.
    Bhuyan, M.K., Ghosh, D., Bora, P.K.: Estimation of 2D motion trajectories from video object planes and its application in hand gesture recognition. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 509–514. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Borgefors, G.: Distance transformations in digital images. Computer Vision, Graphics and Image Processing 34, 344–371 (1986)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • M. K. Bhuyan
    • 1
  • D. Ghosh
    • 2
  • P. K. Bora
    • 1
  1. 1.Department of Electronics and Communication EngineeringIndian Institute of TechnologyGuwahatiIndia
  2. 2.Faculty of Engineering and TechnologyMultimedia UniversityMelaka CampusMalaysia

Personalised recommendations