Skip to main content
SpringerLink
Log in
Book cover

Shape in Picture pp 493–510Cite as

Inference of Stochastic Graph Models for 2-D and 3-D Shapes

  • Conference paper

  • 185 Accesses

Part of the book series: NATO ASI Series ((NATO ASI F,volume 126))

Abstract

Shape interpretation methods that model a shape using stochastic graphs can recognize many classes of nonrigid objects, even if the objects are partially occluded, and interpret complete scenes composed of overlapping nonrigid shapes. These methods can also identify most parts of each shape. This paper describes the use of a stochastic graph as a model for a class of 2-D or 3-D shapes, and presents learning methods that infer stochastic graph models and their symbolic primitives from examples. These methods, as well as a graph-covering method used for scene interpretation, use a criterion of minimum description complexity which eliminates the need for subjective parameters. One practical application of this work is a trainable real-time system that recognizes hand gestures in 2-D images.

This is a preview of subscription content, 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   259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   329.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Barrow, H. G., Ambler, A. P., Burstall, R. M. (1972). Some techniques for recognizing structure in pictures. In: Watanabe, S. (ed.), Frontiers of Pattern Recognition, Academic Press, New York, pp. 1–29.

    Google Scholar 

  2. Biederman, I. (1987). Matching of image edges to object memory, Proc. First Int. Conf. on Computer Vision, London, England: IEEE Comp. Soc. Press, pp. 384–392.

    Google Scholar 

  3. Connel, J. H., Brady, M. (1985). Learning shape descriptions, Proc. 9th Int. Conf. on Artificial Intelligence, Los Angeles, CA, Morgan Kaufmann, pp. 922–925.

    Google Scholar 

  4. Levinson, R. (1985). A self-organizing retrieval system for graphs. Ph.D. Thesis, University of Texas, Austin, TX.

    Google Scholar 

  5. Marr, D. Nishihara, H. K. (1978). Representation and recognition of spatial organization of three-dimensional structures, Proc. R. Soc. B 200, pp. 269–294.

    Article  Google Scholar 

  6. Nackman, L. R. (1984). Two-dimensional critical point configuration graphs, IEEE Trans. on Pattern Analysis and Machine Intelligence, 6, pp. 442–449.

    Article  MATH  Google Scholar 

  7. Rissanen, J. (1978). Modeling by shortest data description, Automatica 14, pp. 465–471.

    Article  MATH  Google Scholar 

  8. Rissanen, J. (1987). Stochastic complexity. J. R. Stat. Soc. 49, pp. 223–239.

    MathSciNet  MATH  Google Scholar 

  9. Segen, J. (1980). Pattern-directed signal analysis, Ph.D. Thesis, Carnegie-Mellon University, Pittsburgh, PA.

    Google Scholar 

  10. Segen, J. (1989). Model Learning and Recognition of Nonrigid Shapes, Proc. Conf. on Computer Vision and Pattern Recognition, San Diego, pp. 597–602.

    Google Scholar 

  11. Segen, J. (1990). Graph clustering and model learning by data compression, Proc. 7th Int. Conf. on Machine Learning, Austin, Texas, pp. 93–101.

    Google Scholar 

  12. Segen, J. (1993). GEST: A learning computer vision system that recognizes hand gestures. In: Michalski, R.S., Tecuci, G. (eds.) Machine Learning IV, Morgan Kaufmann.

    Google Scholar 

  13. Segen, J., Dana, K. (1991). Parallel symbolic recognition of deformable shapes. In: Burkhardt, H., Nuevo, Y., Simon, J.C. (eds.), From Pixels to Features II, North-Holland.

    Google Scholar 

  14. Segen, J., Sanderson, A. C. (1979). A minimal representation criterion for clustering. Proc. 12th Annual Symposium on Comp. Science and Statistics, Univ. of Waterloo, Canada.

    Google Scholar 

  15. Shapiro, L. G. (1980). A structural model of shape. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2, pp. 111–126.

    Article  Google Scholar 

  16. Solomonoff, R. J. (1964). A formal theory of inductive inference I & II, Information and Control, 7, pp. 1–22 & 224–254.

    Google Scholar 

  17. Truve, S., Richards, W. (1987). From Waltz to Winston (via the connection table), Proc. First Int. Conf. on Computer Vision, London, England, IEEE Comp. Soc. Press, pp. 393–404.

    Google Scholar 

  18. Wallace, C. S. Boulton, D. M. (1968). An information measure for classification, Comp. Journal 11 (2), pp. 185–194.

    Article  MATH  Google Scholar 

  19. Winston, P. H. (1975). Learning structural descriptions from examples. In: Winston, P.H. (ed.), The Psychology of Computer Vision, Chapter 5, McGraw Hill, New York.

    Google Scholar 

  20. Wong, A. K. C., You, M. (1985). Entropy and distance of random graphs with application to structural pattern recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 7, pp. 599–609.

    Article  MATH  Google Scholar 

  21. Wong, A. K. C., Lu, S. W. (1985). Recognition and knowledge synthesis of 3-D object images, Proc. Conf. on Computer Vision and Pattern Recognition, San Francisco, pp. 162–166.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. AT&T Bell Laboratories, Holmdel, NJ, 07733, USA

    Jakub Segen

Authors
  1. Jakub Segen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Centre for Mathematics and Computer Science (CWI), Kruislaan 413, 1098 SJ, Amsterdam, The Netherlands

    Ying-Lie O  & Henk J. A. M. Heijmans  & 

  2. Institute for Human Factors (TNO-IZF), Netherlands Organization for Applied Scientific Research, Kampweg 5, 3769 DE, Soesterberg, The Netherlands

    Alexander Toet

  3. Department of Communication and Neuroscience, University of Keele, ST5 5BG, Keele, Staffordshire, UK

    David Foster

  4. Department of Electrical and Computer Engineering, Rutgers University, 08855-0909, Piscataway, NJ, USA

    Peter Meer

Rights and permissions

Reprints and permissions

Copyright information

© 1994 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Segen, J. (1994). Inference of Stochastic Graph Models for 2-D and 3-D Shapes. In: O, YL., Toet, A., Foster, D., Heijmans, H.J.A.M., Meer, P. (eds) Shape in Picture. NATO ASI Series, vol 126. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03039-4_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-03039-4_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-08188-0

  • Online ISBN: 978-3-662-03039-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation