Skip to main content
SpringerLink
Log in

Image Pattern Recognition Using Near Sets

  • Conference paper
Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4482))

Abstract

The problem considered in this paper is how to recognize similar objects based on the detection of patterns in pairs of images. This article introduces a new form of classifier based on approximation spaces in the context of near sets for use in pattern recognition. By way of introducing the basic approach, nonlinear diffusion is used for edge detection and object contour extraction. This form of image transformation makes it possible to compare the contours of objects in pairs of images. Once the contour of an image has been identified, it is then possible to construct approximation spaces based on vectors of probe function measurements associated with selected image features. In this article, the only feature considered is contour, which leads to many contour probe functions. The contribution of this article is a new form of classifier, based on approximation spaces, for use in image pattern recognition.

The authors thank the anonymous reviewers for their very helpful suggestions. This research has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) grant 185986 and Manitoba Hydro grant T277.

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

Access this chapter

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 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Pawlak, Z.: Classification of objects by means of attributes. Polish Academy of Sciences, Technical Report PAS 429 (1981)

    Google Scholar 

  2. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1), 3–27 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  3. Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27(2-3), 245–253 (1996)

    MathSciNet  MATH  Google Scholar 

  4. Peters, J.F.: Near sets. Special theory about nearness of objects. Fundamenta Informaticae 75(1-4), 407–433 (2007)

    MathSciNet  MATH  Google Scholar 

  5. Pavel, M.: Fundamentals of Pattern Recognition. Marcel Dekker, New York (1993)

    MATH  Google Scholar 

  6. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Toronto (2002)

    Google Scholar 

  7. D’Almeida, F.: Nonlinear diffusion toolbox (2003)

    Google Scholar 

  8. Weickert, J.: Anisotropic Diffusion in Image Processing. Ph.d. dissertation, University of Kaiserslautern (1996)

    Google Scholar 

  9. Weickert, J.: Applications of nonlinear diffusion in image processing and computer vision. Acta Mathematica Universitatis Comenianae 70(1), 33–50 (2001)

    MathSciNet  MATH  Google Scholar 

  10. Peters, J.F., Henry, C.: Reinforcement learning with approximations spaces. Fundamenta Informaticae 71(2-3), 323–349 (2006)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba R3T 5V6, Canada

    Christopher Henry & James F. Peters

Authors
  1. Christopher Henry
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James F. Peters
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, York University, M3J 1P3, Toronto, Ontario, Canada

    Aijun An

  2. Institute of Computing Sciences, Poznań University of Technology, ul. Piotrowo 2, 60–965, Poznań, Poland

    Jerzy Stefanowski

  3. Department of Applied Computer Science, University of Winnipeg, R3B 2E9, Winnipeg, Manitoba, Canada

    Sheela Ramanna

  4. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Cory J. Butz

  5. Department of Electrical and Computer Engineering, University of Alberta, T6G 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

  6. Institute of Compuer Science and Technology, Chongqing University of Posts and Telecommunications, 40065, Chongqing, P.R. China

    Guoyin Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Henry, C., Peters, J.F. (2007). Image Pattern Recognition Using Near Sets. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_57

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72530-5_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72529-9

  • Online ISBN: 978-3-540-72530-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation