Skip to main content

Directional Force Field-Based Maps: Implementation and Application

  • Conference paper
Computer Analysis of Images and Patterns (CAIP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5702))

Included in the following conference series:

Abstract

A directional relationship (e.g., right, above) to a reference object can be modeled by a directional map – an image where the value of each point represents how well the relationship holds between the point and the object. As we showed in previous work, such a map can be derived from a force field created by the object (which is seen as a physical entity). This force field-based model, defined by equations in the continuous domain, shows unique characteristics. However, the approximation algorithms that were proposed in the case of 2-D raster data lack efficiency and accuracy. We introduce here new algorithms that correct this flaw, and we illustrate the potential of the force field-based approach through an application to scene matching.

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

Access this chapter

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. Krishnapuram, R., Keller, J.M., Ma, Y.: Quantitative Analysis of Properties and Spatial Relations of Fuzzy Image Regions. IEEE Trans. on Fuzzy Systems 1(3), 222–233 (1993)

    Article  Google Scholar 

  2. Matsakis, P., Wendling, L.: A New Way to Represent the Relative Position between Areal Objects. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(7), 634–643 (1999)

    Article  Google Scholar 

  3. Bloch, I.: Fuzzy Relative Position Between Objects in Image Processing: A Morphological Approach. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(7), 657–664 (1999)

    Article  Google Scholar 

  4. Franklin, N., Henkel, L.A., Zangas, T.: Parsing Surrounding Space into Regions. Memory & Cognition 23(4), 397–407 (1995)

    Google Scholar 

  5. Logan, G.D., Sadler, D.D.: A Computational Analysis of the Apprehension of Spatial Relations. Language and Space, pp. 493–529. MIT Press, Cambridge (1996)

    Google Scholar 

  6. Carlson-Radvansky, L.A., Logan, G.D.: The Influence of Reference Frame Selection on Spatial Template Construction. Memory & Language 37(3), 411–437 (1997)

    Article  Google Scholar 

  7. Gapp, K.-P.: Angle, Distance, Shape, and Their Relationship to Projective Relations. In: Proc. 17th Conf. Cognitive Science Soc., pp. 112–117 (1995)

    Google Scholar 

  8. Frank, A.U.: Qualitative Spatial Reasoning: Cardinal Directions as an Example. Int. J. of Geographical Information Systems 10(3), 269–290 (1996)

    Google Scholar 

  9. Matsakis, P., Ni, J., Veltman, M.: Directional Relationships to a Reference Object: A Quantitative Approach based on Force Fields. Submitted to ICIP 2009 (2009)

    Google Scholar 

  10. Matsakis, P., Ni, J., Wang, X.: Object Localization based on Directional Information: Case of 2D Raster Data. In: Proc. 18th Int. Conf. on Pattern Recognition, vol. 2, pp. 142–146 (2006)

    Google Scholar 

  11. Bloch, I., Saffiotti, A.: On the Representation of Fuzzy Spatial Relations in Robot Maps. In: Bouchon-Meunier, B., Foulloy, L., Yager, R.R. (eds.) Intelligent Systems for Information Processing, pp. 47–57. Elsevier, NL (2003)

    Chapter  Google Scholar 

  12. Colliot, O., Camara, O., Bloch, I.: Integration of Fuzzy Spatial Relations in Deformable Models-Application to Brain MRI Segmentation. Pattern Recognition 39, 1401–1414 (2006)

    Article  Google Scholar 

  13. Krishnapuram, R., Medasani, S., Jung, S.-H., Choi, Y.-S., Balasubramaniam, R.: Content-based Image Retrieval Based on a Fuzzy Approach. IEEE Trans. on Knowledge and Data Engineering 16(10), 1185–1199 (2004)

    Article  Google Scholar 

  14. Smith, G.B., Bridges, S.M.: Fuzzy Spatial Data Mining. In: Proc. NAFIPS, pp. 184–189 (2002)

    Google Scholar 

  15. Matsakis, P., Keller, J., Wendling, L., Marjamaa, J., Sjahputera, O.: Linguistic Description of Relative Positions in Images. TSMC Part B 31(4), 573–588 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ni, J., Veltman, M., Matsakis, P. (2009). Directional Force Field-Based Maps: Implementation and Application. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-03767-2_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03766-5

  • Online ISBN: 978-3-642-03767-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics