Skip to main content

On the Relationship Between Active Contours and Contextual Classification

  • Conference paper
Computer Recognition Systems

Part of the book series: Advances in Soft Computing ((AINSC,volume 30))

Abstract

To discuss the relationship between active contours and contextual classification, a formal definition of the contour as well as a uniform approach to the all active contour methods are proposed first, and then a contextual classification problem is introduced and formalized. The equivalence relationship between contours and classifiers, thoroughly considered and illustrated by examples, proves to allow incorporation of the methods and techniques specific for the active contour approach to the contextual classification and vice versa.

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 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

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. Kass M., Witkin W., Terzopoulos D., (1988) Snakes: Active Contour Models, International Journal of Computer Vision, 321–331

    Google Scholar 

  2. Caselles V., Kimmel R., Sapiro G., (1997) Geodesic Active Contours, International Journal of Computer Vision 22(1) 61–79

    Article  MATH  Google Scholar 

  3. Xu Ch., Yezzi A., Prince J., (2000) On the Relationship between Parametric and Geometric Active Contours, in Proc. of 34th Asilomar Conference on Signals, Systems and Computers 483–489

    Google Scholar 

  4. Cootes T., Taylor C., Cooper D., Graham J., (1994) Active Shape Model-Their Training and Application, CVGIP Image Understanding, 61(1) 38–59 Janvier

    Google Scholar 

  5. Grzeszczuk R., Levin D., (1997) Brownian Strings: Segmenting Images with Stochastically Deformable Models, IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 19 no. 10 1100–1013

    Article  Google Scholar 

  6. Tadeusiewicz R., Flasinski M., (1991) Pattern Recognition, PWN, Warsaw (in Polish)

    Google Scholar 

  7. Kwiatkowski W., (2001) Methods of Automatic Pattern Recognition, WAT, Warsaw (in Polish)

    Google Scholar 

  8. Sobczak W., Malina W., (1985) Methods of Information Selection and Reduction, WNT, Warsaw (in Polish)

    Google Scholar 

  9. Nikolaidis N., Pitas I., (2001) 3-D Image Processing Algorithms, John Wiley and Sons Inc., New York

    Google Scholar 

  10. Bishop Ch., (1993) Neural Networks for Pattern Recognition, Clarendon Press, Oxford

    Google Scholar 

  11. Pal S., Mitra S., (1999) Neuro-fuzzy Pattern Pecognition, Methods in Soft Computing, John Wiley and Sons Inc., New York

    Google Scholar 

  12. Bennamoun M., Mamic G., (2002) Object Recognition, Fundamental and Case Studies, Springer-Verlag, London

    Google Scholar 

  13. Looney C., (1997) Pattern Recognition Using Neural Networks, Theory and Algorithms for Engineers and Scientists, Oxford University Press, New York

    Google Scholar 

  14. Sonka M., Hlavec V., Boyle R., (1994) Image Processing, Analysis and Machine Vision, Chapman and Hall, Cambridge

    Google Scholar 

  15. Gonzalez R., Woods R., (2002) Digital Image Processing, Prentice-Hall Inc., New Jersey

    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

Tomczyk, A., Szczepaniak, P.S. (2005). On the Relationship Between Active Contours and Contextual Classification. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_35

Download citation

  • DOI: https://doi.org/10.1007/3-540-32390-2_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25054-8

  • Online ISBN: 978-3-540-32390-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics