Scale Space Based Grammar for Hand Detection

  • Jan Prokaj
  • Niels da Vitoria Lobo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4153)

Abstract

For detecting difficult objects, like hands, we present an algorithm that uses tokens and a grammar. Tokens are found by employing a new scale space edge detector that finds scale invariant features at object boundaries. We begin by constructing the scale space. Then we find edges at each scale and flatten the scale space to one edge image. To detect a hand we define a hand pattern grammar using curve tokens for finger tips and wedges, and line tokens. We identify a hand pattern by parsing these tokens using a graph based algorithm. We show and discuss the results of this algorithm on a database of hand images.

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References

  1. 1.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 92–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Pavlovic, V.I., Sharma, R., Huang, T.S.: Visual interpretation of hand gestures for human-computer interaction: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 677–695 (1997)CrossRefGoogle Scholar
  3. 3.
    Stenger, B., Mendonca, P., Cipolla, R.: Model-based 3d tracking of an articulated hand. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p. 310 (2001)Google Scholar
  4. 4.
    Yuan, Q., Sclaroff, S., Athitsos, V.: Automatic 2d hand tracking in video sequences. In: Seventh IEEE Workshop on Application of Computer Vision, pp. 250–256 (2005)Google Scholar
  5. 5.
    Wu, Y., Huang, T.: View-independent recognition of hand postures. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2088–2094 (2000)Google Scholar
  6. 6.
    Athitsos, V., Sclaroff, S.: Estimating 3d hand pose from a cluttered image. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 432–439 (2003)Google Scholar
  7. 7.
    Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)CrossRefGoogle Scholar
  8. 8.
    Lindeberg, T.: Scale-space theory in computer vision. Kluwer, Boston (1994)Google Scholar
  9. 9.
    Garcia, J., da Vitoria Lobo, N., Shah, M., Feinstein, J.: Automatic detection of heads in colored images. In: Second Canadian Conference on Computer and Robot Vision, 276–281 (2005)Google Scholar
  10. 10.
    Burns, J.B., Hanson, A., Riseman, E.M.: Extracting straight lines. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 425–455 (1986)CrossRefGoogle Scholar
  11. 11.
    Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1988)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jan Prokaj
    • 1
  • Niels da Vitoria Lobo
    • 1
  1. 1.University of Central FloridaOrlandoUSA

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