Skip to main content
SpringerLink
Log in

Which Shape Representation Is the Best for Real-Time Hand Interface System?

  • Conference paper
Advances in Visual Computing (ISVC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5875))

Included in the following conference series:

Abstract

Hand is a very convenient interface for immersive human-computer interaction. Users can give commands to a computer by hand signs (hand postures, hand shapes) or hand movements (hand gestures). Such a hand interface system can be realized by using cameras as input devices, and software for analyzing the images. In this hand interface system, commands are recognized by analyzing the hand shapes and its trajectories in the images. Therefore, success of the recognition of hand shape is vital and depends on the discriminative power of the hand shape representation. There are many shape representation techniques in the literature. However, none of them are working properly for all shapes. While a representation leads to a good result for a set of shapes, it may fail in another one. Therefore, our aim is to find the most appropriate shape representation technique for hand shapes to be used in hand interfaces. Our candidate representations are Fourier Descriptors, Hu Moment Invariant, Shape Descriptors and Orientation Histogram. Based on widely-used hand shapes for an interface, we compared the representations in terms of their discriminative power and speed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. La Viola, J.J.: A Survey of Hand Posture and Gesture Recognition Techniques and Technology. Technical Report CS-99-11, Department of Computer Science, Brown University (1999)

    Google Scholar 

  2. Erol, A., Bebis, G., Nicolescu, M., Boyle, R.D., Twombly, X.: A Review on Vision Based Full DOF Hand Motion Estimation. In: IEEE Workshop on Vision for Human-Computer Interaction (in conjunction with CVPR 2005), San Diego, CA, June 21 (2005)

    Google Scholar 

  3. Wu, Y., Huang, T.S.: Hand Modeling, Analysis and Recognition. IEEE Signal Processing Magazine, 51–58 (May 2001)

    Google Scholar 

  4. Quek, F.K.H., Mysliwiec, Y., Zhao, M.: FingerMouse: a Freehand Pointing Interface. In: Proc. of Int. Workshop on Automatic Face and Gesture Recognition, pp. 372–377 (1995)

    Google Scholar 

  5. Genc, S., Atalay, V.: ITouch: Vision-based Intelligent Touch Screen in a Distributed Environment. In: Int. Conf. on Multimodal Interfaces (ICMI), Doctoral Spotlight (October 2005)

    Google Scholar 

  6. Licsar, A., Sziranyi, T.: Dynamic Training of Hand Gesture Recognition System. In: Proc. Intl. Conf. on Pattern Recognition, ICPR 2004, vol. 4, pp. 971–974 (2004)

    Google Scholar 

  7. Freeman, W.T., Anderson, D., Beardsley, P., Dodge, C., Kage, H., Kyuma, K., Miyake, Y., Roth, M., Tanaka, K., Weissman, C., Yerazunis, W.: Computer Vision for Interactive Computer Graphics. IEEE Computer Graphics and Applications 18(3), 42–53 (1998)

    Article  Google Scholar 

  8. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Hoboken (2000)

    Google Scholar 

  9. Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37(1), 1–19 (2004)

    Article  MATH  Google Scholar 

  10. Zhang, D., Lu, G.: Content-Based Shape Retrieval Using Different Shape Descriptors: A Comparative Study. In: Proc. of IEEE Conference on Multimedia and Expo., Tokyo, August 2001, pp. 317–320 (2001)

    Google Scholar 

  11. Freeman, W.T., Roth, M.: Orientation Histograms for Hand Gesture Recognition. In: Intl. Workshop on Automatic Face and Gesture Recognition (1995)

    Google Scholar 

  12. Peura, M., Livarinen, J.: Efficiency of Simple Shape Descriptors. In: Aspects of Visual Form, pp. 443–451. World Scientific, Singapore (1997)

    Google Scholar 

  13. Flusser, J.: Moment Invariants in Image Analysis. In: Proceedings of World Academy of Science, Engineering and Technology, February 2006, vol. 11 (2006) ISSN 1307-6884

    Google Scholar 

  14. Fitzgibbon, A., Pilu, M., Fisher, R.B.: Direct Least Square Fitting of Ellipses. Pattern Analysis and Machine Intelligence 21(5) (May 1999)

    Google Scholar 

  15. Zhang, D., Lu, G.: A Comparative Study on Shape Retrieval Using Fourier Descriptors with Different Shape Signatures. In: Proc. of International Conference on Intelligent Multimedia and Distance Education, Fargo, ND, USA, pp. 1–9 (2001)

    Google Scholar 

  16. Hu, M.K.: Visual Pattern Recognition by Moment Invariants. IRE Transactions on Information Theory IT-8, 179–187 (1962)

    Google Scholar 

  17. Gary, K.A.: Learning OpenCV. O’Reilly, Sebastopol (2008) (first print)

    Google Scholar 

  18. Chen, F.S., Fu, C.M., Huang, C.L.: Hand Gesture Recognition Using a Real-time Tracking Method and Hidden Markov Models. Image and Vision Computing 21, 745–758 (2003)

    Article  Google Scholar 

  19. Kolb, A., Barth, E., Koch, R.: ToF-sensors: New dimensions for realism and interactivity. In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Workshop on ToF Camera based Computer Vision (TOF-CV), pp. 1–6 (2008), doi:10.1109/CVPRW

    Google Scholar 

  20. Gvili, R., Kaplan, A., Ofek, E., Yahav, G.: Depth Keying, http://www.3dvsystems.com/technology/DepthKey.pdf

  21. Microsoft Natal Project: http://www.xbox.com/en-US/live/projectnatal

  22. Nakagawa, S., Nakanishi, H.: Speaker-Independent English Consonant and Japanese word recognition by a Stochastic Dynamic Time Warping Method. Journal of Institution of Electronics and Telecommunication Engineers 34(1), 87–95 (1988)

    Google Scholar 

  23. Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Real-Time Tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), pp. 246–252 (1999)

    Google Scholar 

  24. Jones, M.J., Rehg, J.M.: Statistical Color Models with Application to Skin Detection. Int. J. of Computer Vision 46(1), 81–96 (2002)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Technology, Information Systems, Bilkent University, Ankara, Turkey

    Serkan Genç

  2. Department of Computer Engineering, METU, Ankara, Turkey

    Volkan Atalay

Authors
  1. Serkan Genç
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Volkan Atalay
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, Saitama, Japan

    Yoshinori Kuno

  6. Institute for Infocomm Research, 21 Heng Mui Keng Terrace, P.O. Box, 119613, Singapore

    Junxian Wang

  7. Department of Computer Science & Information Engineering, Tamkang University, Tamsui, Taipei, Taiwan, R.O.C.

    Jun-Xuan Wang

  8. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  9. Department of Informatics, Univ. of Zurich, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  10. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  11. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  12. Humana Inc., 40202, Louisville, KY, USA

    Miguel L. Encarnação

  13. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  14. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Genç, S., Atalay, V. (2009). Which Shape Representation Is the Best for Real-Time Hand Interface System?. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-10331-5_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10330-8

  • Online ISBN: 978-3-642-10331-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation