A Probabilistic Framework for Articulated Shape Recognition

  • Abdullah A. Al-Shaher
  • Edwin. R. Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3179)

Abstract

This paper describes a probabilistic framework for recognising 2D shapes with articulated components. The shapes are represented using both geometrical and a symbolic primitives, that are encapsulated in a two layer hierarchical architecture. Each primitive is modelled so as to allow a degree of articulated freedom using a polar point distribution model that captures how the primitive movement varies over a training set. Each segment is assigned a symbolic label to distinguish its identity, and the overall shape is represented by a configuration of labels. We demonstrate how both the point-distribution model and the symbolic labels can be combined to perform recognition using a probabilistic hierarchical algorithm. This involves recovering the parameters of the point distribution model that minimise an alignment error, and recovering symbol configurations that minimise a structural error. We apply the recognition method to human pose recognition.

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References

  1. 1.
    Cootes, T., Taylor, C.: Combining point distribution models with shape models based on finite element analysis. IVC 13(5), 403–409 (1995)Google Scholar
  2. 2.
    Duta, N., Jain, A., Dubuisson, P.: Learning 2d shape models. In: International Conference on Computer Vision and pattern Recognition, vol. 2, pp. 8–14 (1999)Google Scholar
  3. 3.
    Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: Proc. ECCV, pp. 343–356 (1996)Google Scholar
  4. 4.
    Gonzales, J., Varona, J., Roca, F.X., Villanueva, J.: aspace:Action space for recognition and synthesis of human actions. In: 2 IWAMDO, Spain, pp. 189–200 (2002)Google Scholar
  5. 5.
    Rehg, J.M., Kanade, T.: Visual tracking of high dof articulated structures: an application to human hand tracking. In: 3 ECCV, Sweden, pp. 35–46 (1994)Google Scholar
  6. 6.
    Heap, T., Hogg, D.: Extending the point distribution model using polar coordinates. Image and Vision Computing 14, 589–599 (1996)CrossRefGoogle Scholar
  7. 7.
    Cootes, T., Taylor, C.: A mixture models for representing shape variation. Image and Vision Computing 17, 403–409 (1999)CrossRefGoogle Scholar
  8. 8.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. Journal of Royal Statistical Soc. Ser. 39, 1–38 (1977)MATHMathSciNetGoogle Scholar
  9. 9.
    Jordan, M., Jacobs, R.: Hierarchical mixtures of experts and the em algorithm. Neural Computation 6, 181–214 (1994)CrossRefGoogle Scholar
  10. 10.
    Hancock, E.R., Kittler, J.: Edge-labelling using dictionary-based relaxation. IEEE Transaction on PAMI 12(2), 165–181 (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Abdullah A. Al-Shaher
    • 1
  • Edwin. R. Hancock
    • 1
  1. 1.University of YorkYorkUK

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