Skip to main content

Application of Ants Ideas on Image Edge Detection

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9374))

Abstract

The aim of the image edge detection is to find the points, in a digital image, at which the brightness level changes sharply. Normally they are curved lines called edges. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction. Edge detection may lead to finding the boundaries of objects. It is one of the fundamental steps in image analysis. Edge detection is a hard computational problem. In this paper we apply a multiagent system. The idea comes from ant colony optimization. We use the swarm intelligence of the ants to search the image edges.

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 EPUB and 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

References

  1. Baterina, A.V., Oppus, C.: Image edge detection using ant colony optimization. WSEAS Trans. Sig. Process. 6(8), 58–67 (2010)

    Google Scholar 

  2. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)

    MATH  Google Scholar 

  3. Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 8(6), 679–697 (1986)

    Article  Google Scholar 

  4. Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  5. Fidanova, S., Atanasov, K.: Generalized net model for the process of hybrid ant colony optimization. C. R. l’Academie. Bulgare Sci. 62(3), 315–322 (2009)

    MATH  Google Scholar 

  6. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall Inc., Upper Saddle River (2002)

    Google Scholar 

  7. Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall Inc, Upper Saddle River (1989)

    MATH  Google Scholar 

  8. Jevtic, A., Li, B.: Ant algorithm for adaptive edge detection. In: Abrao, T. (ed.) Search Algorithms for Engineering Optimization, Chapter 2, INTECH publisher (2013)

    Google Scholar 

  9. Mlsna, P.A., Rodriguez, J.J.: Gradient and laplacian-type edge detection. In: Bovik, A. (ed.) Handbook of Image and Video Processing, pp. 415–431. Academic Press, San Diego (2000)

    Google Scholar 

  10. Nezamabadi-pour, H., Saryazdi, S., Rashedi, E.: Edge detection using ant algorithm. Soft Comput. 10(7), 623–628 (2006)

    Article  Google Scholar 

  11. Pratt, W.K.: Digital Image Processing, 2nd edn. Wiley, New York (1991)

    MATH  Google Scholar 

  12. Tian, J., Yu, W., Xie, S.: An ant colony optimization algorithm for image edge detection. In: IEEE Congress on Evolutionary Computation, pp. 751–756. Hong Kong (2008)

    Google Scholar 

  13. Zhou, P., Ye, W.Q., Wang, Q.: An improved canny algorithm for edge detection. J. Comput. Inf. Syst. 7(5), 1516–1523 (2011)

    Google Scholar 

  14. Zhang, Z., Ma, S., Liu, H., Gong, Y.: An edge detection approach based on directional wavelet transform. J. Comput. Math. Appl. 57(8), 1265–1271 (2009)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by the Bulgarian National Scientific Fund under the grants DFNI 02/20 “Efficient Parallel Algorithms for Large Scale Computational Problems” and DFNI 02/5 “InterCriteria Analysis. A New Approach to Decision Making” and by EC grant AcomIn.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stefka Fidanova .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Fidanova, S., Ilcheva, Z. (2015). Application of Ants Ideas on Image Edge Detection. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2015. Lecture Notes in Computer Science(), vol 9374. Springer, Cham. https://doi.org/10.1007/978-3-319-26520-9_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26520-9_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26519-3

  • Online ISBN: 978-3-319-26520-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics