Skip to main content

A Graph-Based Concept for Spatiotemporal Information in Cognitive Vision

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3434))

Abstract

A concept relating story-board description of video sequences with spatio-temporal hierarchies build by local contraction processes of spatio-temporal relations is presented. Object trajectories are curves in which their ends and junctions are identified. Junction points happen when two (or more) trajectories touch or cross each other, which we interpret as the “interaction” of two objects. Trajectory connections are interpreted as the high level descriptions.

Supported by the Austrian Science Fund under grant FSP-S9103-N04.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allen, J.: An Interval-based Representation of Temporal Knowledge. In: Proc. 7th Inter. Joint Conf. on AI, pp. 221–226 (1981)

    Google Scholar 

  2. Balder, N.I.: Temporal Scene Analysis: Conceptual Descriptions of Object Movements. PhD thesis, University of Toronto, Canada (1975)

    Google Scholar 

  3. Brun, L., Kropatsch, W.G.: The Construction of Pyramids with Combinatorial Maps. Technical Report PRIP-TR-63, Pattern Recognition and Image Processing Group, TU Wien, Austria (2000)

    Google Scholar 

  4. Brun, L., Kropatsch, W.G.: Introduction to Combinatorial Pyramids. In: Bertrand, G., Imiya, A., Klette, R. (eds.) Digital and Image Geometry. LNCS, vol. 2243, pp. 108–128. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  5. Burge, M., Kropatsch, W.G.: A Minimal Line Property Preserving Representation of Line Images. Comp., Devoted Issue on Im. Proc. 62, 355–368 (1999)

    MATH  Google Scholar 

  6. Chella, A., Frixione, M., Gaglio, S.: Understanding Dynamic Scenes. Artificial Intelligence 123, 89–132 (2000)

    Article  MATH  Google Scholar 

  7. Clarke, B.: A Calculus of Individuals Based on Connection. Notre Dame J. of Formal Logic 23(3), 204–218 (1981)

    Article  Google Scholar 

  8. Clarke, B.: Individuals and Points. Notre Dame J. of For. Log. 26(1), 61–75 (1985)

    Article  MATH  Google Scholar 

  9. Haxhimusa, Y., Glantz, R., Kropatsch, W.G.: Constructing Stochastic Pyramids by MIDES - Maximal Independent Directed Edge Set. In: Hancock, E.R., Vento, M. (eds.) GbRPR 2003. LNCS, vol. 2726, pp. 35–46. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  10. Haxhimusa, Y., Glantz, R., Saib, M., Langs, G., Kropatsch, W.G.: Logarithmic Tapering Graph Pyramid. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 117–124. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  11. Kropatsch, W.G.: Property Preserving Hiearchical Graph Transformation. In: Arricelli, C., Cordella, L., di Baja, G.S. (eds.) Advances in Visual Form Analysis, Singapore, pp. 340–349. World Scientific, Singapore (1998)

    Google Scholar 

  12. Meer, P.: Stochastic image pyramids. Computer Vision, Graphics, and Image Processing 45(3), 269–294 (1989)

    Article  Google Scholar 

  13. Randell, D., Cui, Z., Cohn, A.: A Spatial Logic Based on Regions and Connection. In: Proc. 3rd Int. Conf. on Knowledge Representation and Reasoning, pp. 165–176. Morgan Kaufmann, San Francisco (1992)

    Google Scholar 

  14. Sablatnig, R.: Increasing flexibility for automatic visual inspection: the general analysis graph. Mach. Vision Appl. 12(4), 158–169 (2000)

    Article  Google Scholar 

  15. A Reasearch Roadmap of Cognitive Vision (DRAFT Version 3.2). In: Vernon, D. (ed.) ECVision: The European Research Network for Cognitive Computer Vision Systems (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ion, A., Haxhimusa, Y., Kropatsch, W.G. (2005). A Graph-Based Concept for Spatiotemporal Information in Cognitive Vision. In: Brun, L., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2005. Lecture Notes in Computer Science, vol 3434. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31988-7_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-31988-7_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25270-2

  • Online ISBN: 978-3-540-31988-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics