Skip to main content
SpringerLink
Log in

Gray Scale Image Edge Detection Using Rough Sets

  • Chapter
  • First Online:
Soft Computing for Image and Multimedia Data Processing

Abstract

An image may be defined as a two-dimensional function, f(x, y), where x and y are the spatial (planar) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When the co-ordinates (x, y) and the amplitude values of f are all finite and discrete quantities, we can call the image as a digital image.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
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
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Z. Pawlak, Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  2. S. Bhattacharyya, U. Maulik, P. Dutta, High-speed target tracking by fuzzy hostility-induced segmentation of optical flow field. Int. J. Appl. Soft Comput. 9, 126–134 (2009)

    Article  Google Scholar 

  3. S. Bhattacharyya, P. Dutta, XMUBET with CONSENT: a pixel hostility induced multiscale object extractor, in Proceedings of IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP’04), Melbourne, Australia, Dec 2004, pp. 277–282

    Google Scholar 

  4. S. Bhattacharyya, P. Dutta, Multiscale object extraction with MUSIG and MUBET with CONSENT: a comparative study, in Proceedings of KBCS 2004, Hyderabad, India, Dec 2004, pp. 100–109

    Google Scholar 

  5. A. Deluca, S. Termini, A definition of non-probabilistic entropy in the setting of fuzzy set theory. Inf. Control 20, 301–312 (1972)

    Article  MathSciNet  Google Scholar 

  6. R.C. Gonzalez, R.E. Woods, Digital Image Processing (Prentice-Hall, Englewood Cliffs, 2002)

    Google Scholar 

  7. B.J.T. Fernandes, G.D.C. Cavalcanti, T.I. Ren, Classification and segmentation of visual patterns based on receptive and inhibitory fields, in Proceedings of the 8th International Conference on Hybrid Intelligent Systems, Barcelona, 2008, pp. 126–131

    Google Scholar 

  8. J. Canny, A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–714 (1986)

    Article  Google Scholar 

  9. T. Lindeberg, Edge detection and ridge detection with automatic scale selection. Int. J. Comput. Vis. 30(2), 117–154 (1998)

    Article  Google Scholar 

  10. R.M. Haralick, L.G. Shapiro, Computer and Robot Vision, vol. 1 (Addison-Wesley, Reading, MA, 1992)

    Google Scholar 

  11. H. Chidiac, D. Ziou, Classification of image edges, in Proceedings of Vision Interface’99, Canada, 1999, pp. 17–24

    Google Scholar 

  12. G. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications (Prentice Hall, New Delhi, 2005)

    Google Scholar 

  13. A. Lorette, X. Descombes, J. Zerubia, Fully unsupervised fuzzy clustering with entropy criterion, in Proceedings of International Conference on Pattern Recognition (ICPR’00), Barcelona, 2000, vol. 3, pp. 3998–4001

    Google Scholar 

  14. S.K. Pal, R.A. King, A.A. Hashim, Automatic grey level thresholding through index of fuzziness and entropy. Pattern Recognit. Lett. 1(3), 141–146 (1983)

    Article  Google Scholar 

  15. S.K. Pal, A. Ghosh, Image segmentation using fuzzy correlation. Inf. Sci. 62(3), 223–250 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  16. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum Press, New York, 1981)

    Book  MATH  Google Scholar 

  17. Z. Pawlak, Rough sets, decision algorithms and Bayes’ theorem. Eur. J. Oper. Res. 136, 181–189 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  18. Z. Pawlak, Rough sets and Boolean reasoning. Inf. Sci. 177, 41–73 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  19. J. Jiang, D. Yang, H.E. Wei, Image segmentation based on rough set theory and neural networks, in Proceedings of VIE08, Michael Faraday House, Six Hills Way, Stevenage, Herts SG1 2AY, 2008, pp. 361–365

    Google Scholar 

  20. D. Sen, S.K. Pal, Generalized rough sets, entropy, and image ambiguity measures. IEEE Trans. Syst. Man Cybern. B 39(1), 117–128 (2009)

    Article  Google Scholar 

  21. D. Sen, S.K. Pal, Histogram thresholding using beam theory and ambiguity measures. Fundam. Inform. 75(1–4), 483–504 (2007)

    MathSciNet  Google Scholar 

  22. D. Sen, S.K. Pal, Measuring ambiguities in images using rough and fuzzy set theory, in Proceedings of IEEE International Conference on Signal Processing, Communications and Networking, Chennai, India, 2008, pp. 333–338

    Google Scholar 

  23. A. Chowdhury, H. Bhaumik, B. Chakraborty, S. Bhattacharyya, Image edge detection using pixel roughness, in Proceedings of National Conference on Ubiquitous Computing (NCUBQT 2009), Kolkata, 2009, pp. 18–23

    Google Scholar 

  24. A. Chowdhury, S. Bhattacharyya, Image edge detection using rough pixel neighborhood hostility index, in Proceedings of International Conference on Emerging Trends in Computer Science, Communication and Information Technology (CSCIT’10), Nanded, 2010

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Kolkata, India

    Siddhartha Bhattacharyya

  2. Department of Computer Science & Engineering, Jadavpur University, Kolkata, India

    Ujjwal Maulik

Authors
  1. Siddhartha Bhattacharyya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ujjwal Maulik
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Bhattacharyya, S., Maulik, U. (2013). Gray Scale Image Edge Detection Using Rough Sets. In: Soft Computing for Image and Multimedia Data Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40255-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40255-5_8

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40254-8

  • Online ISBN: 978-3-642-40255-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation