CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Automatic Image Segmentation using Ultra Fuzziness

by Ch.v. Narayana, E. Sreenivasa Reddy, M. Seetharama Prasad
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 12
Year of Publication: 2012
Authors: Ch.v. Narayana, E. Sreenivasa Reddy, M. Seetharama Prasad
10.5120/7677-0977

Ch.v. Narayana, E. Sreenivasa Reddy, M. Seetharama Prasad . Automatic Image Segmentation using Ultra Fuzziness. International Journal of Computer Applications. 49, 12 ( July 2012), 6-13. DOI=10.5120/7677-0977

@article{ 10.5120/7677-0977,
author = { Ch.v. Narayana, E. Sreenivasa Reddy, M. Seetharama Prasad },
title = { Automatic Image Segmentation using Ultra Fuzziness },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 12 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number12/7677-0977/ },
doi = { 10.5120/7677-0977 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:46:04.851775+05:30
%A Ch.v. Narayana
%A E. Sreenivasa Reddy
%A M. Seetharama Prasad
%T Automatic Image Segmentation using Ultra Fuzziness
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 12
%P 6-13
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, an automatic histogram threshold approach based on a fuzzy measure is presented. This work is an improvement of an existing method. Using fuzzy logic concepts, the problems involved in finding the minimum/maximum of a entropy criterion function are avoided. Hamid R Tizhoosh defined a membership function to measure the image fuzziness, which makes the methodology totally supervised. We attempt to automate the process by taking an alternate approach. For low contrast images contrast enhancement is assumed. Experimental results demonstrate a quantitative improvement.

References
  1. W. K. Pratt, Digital Image Processing, third ed. New York: Wiley, 2001.
  2. R. C. Gonzalez and R. E. Woods, Digital Image Processing. Reading, MA: Addison-Wesley, 1993.
  3. N. R. Pal and S. K. Pal, "A review on image segmentation techniques", pattern recog. ,vol. 26,No. 9, pp. 1277-1294,1993.
  4. Y. J. Zhang, "A survey on evaluation methods for image segmentation," Computer Vision and Pattern Recognition, vol. 29, pp. 1335–1346, 1996.
  5. M. Sezgin and B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation," J. Electron. Imag. , vol. 13, no. 1, pp. 146–165, Jan. 2004.
  6. N. Otsu, "A threshold selection method from gray level histograms," IEEE Trans. Syst. , Man, Cybern. , vol. SMC-9, pp. 62–66, 1979.
  7. T. Ridler and S. Calvard, "Picture thresholding using an iterative selection method," IEEE Trans. Syst. , Man, Cybern. , vol. SMC-8, pp. 630–632, Aug. 1978.
  8. J. Kittler and J. Illingworth, "Minimum error thresholding," Pattern Recognit. , vol. 19, no. 1, 1986.
  9. J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, "A new method for graylevel picture thresholding using the entropy of the histogram," Graph. Models Image Process. , vol. 29, pp. 273–285, 1985.
  10. T. Pun, "A new method for gray-level picture thresholding using the entropy of the histogram," Signal Process. , vol. 2, no. 3, pp. 223–237, 1980.
  11. Yang Xiao, Zhiguo Cao, Tianxu Zhang "Entropic thresholding based on gray level spatial correlation histogram", IEEE trans. 19th international conf. , pp. 1-4,ICPR-2008.
  12. Y. Xiao, Z. G. Cao, and S. Zhong, "New entropic thresholding approach using gray-level spatial correlation histogram", Optics Engineering, 49, 127007, 2010
  13. M Seetharama Prasad, T Divakar, L S S Reddy, "Improved Entropic Thresholding based on GLSC histogram with varying similarity measure", International Journal of Computer Applications, vol. 23 no. 1, June 2011, pp. 25-32.
  14. Yang Xiao, Zhiguo Cao,Wen zhuo, "Type-2 fuzzy thresholding using GLSC histogram of human visual nonlinearity characteristics", Optics Express, vol. 19, no. 11, 10657, May 2011.
  15. L. A. Zadeh,"Fuzzy sets", Inf. Control 8, 338-353,1965
  16. C. Murthy and S. Pal, "Fuzzy thresholding: Mathematical framework, bound functions and weighted moving average technique," Pattern Recognit. Lett. , vol. 11, pp. 197–206, 1990.
  17. O. J. Tobias, R. Seara, and F. A. P. Soares, "Automatic image segmentation using fuzzy sets," in Proc. 38th Midwest Symp. Circuits and Systems, 1996, vol. 2, pp. 921–924.
  18. O. J. Tobias and R. Seara, "Image segmentation by histogram thresholding using fuzzy sets," IEEE Trans. Image Process. , vol. 11, 2002.
  19. A. S. Pednekar and I. A. Kakadiaris, "Image segmentation based on fuzzy connectedness using dynamic weights," IEEE Trans. Image Process. , vol. 15, no. 6, pp. 1555–1562, Jun. 2006.
  20. F. Sahba and H. R. Tizhoosh, "Quasi-Global Oppositional Fuzzy Thresholding" in Proc. IEEE International Conference on Fuzzy Systems (FUZZ–IEEE), Korea, August 20-24, 2009, pp. 1346-1351.
  21. C. V. Jawahar, P. K. Biswas, and A. K. Ray, "Investigations on fuzzy thresholding based on fuzzy clustering", Pattern Recogn. 30(10) pp. 1605–1613,1997.
  22. K. S. Chuang, H. L. Tzeng, S. Chen, J. Wu, and T. J. Chen, "Fuzzy c-means clustering with spatial information for image segmentation," Comput. Med. Imag. Graph. , vol. 30, no. 1, pp. 9–15, 2006.
  23. S. Sahaphong and N. Hiransakolwong, "Unsupervised image segmentation using automated fuzzy c-means," in Proc. IEEE Int. Conf. Computer and Information Technology, pp. 690–694, Oct. 2007.
  24. A. Kaufmann, Introduction to the Theory of Fuzzy Subsets. New York: Academic, 1975, vol. I.
  25. N. R. Pal and J. C. Bezdek, "Measuring fuzzy uncertainty," IEEE Trans. Fuzzy Syst. , vol. 2, 1994.
  26. H. R. Tizhoosh, "Image thresholding using type II fuzzy sets," Pattern Recognit. , vol. 38, pp. 2363–2372, 2005.
  27. Nuno Vieira Lopes et al. "Automatic Histogram Threshold using Fuzzy Measures" IEEE Trans. Image Process. , vol. 19, no. 1, Jan. 2010.
  28. L. K. Huang and M. J. J. Wang, "Image thresholding by minimizing the measures of fuzziness," Pattern Recognit. , vol. 28, no. 1, pp. 41–51, 1995.
  29. Fares S. Al-Qunaieer, Hamid R Tizoosh, Shahryar Rahnamayan "Oppositional Fuzzy Image Thresholding," 978-1-4244-8126-2/10 IEEE, 2010.
  30. F. Sahba and H. R. Tizhoosh, "Quasi-Global Oppositional Fuzzy Thresholding" in Proc. IEEE International Conference on Fuzzy Systems (FUZZ – IEEE), Korea, August 20-24, 2009, pp. 1346-1351.
  31. M Seetharama Prasad et al. "Unsupervised Image thresholding using Fuzzy Measures", International Journal of Computer Applications, vol. 27 no. 2, August 2011, pp. 32-41.
  32. Paul Jaccard, "Etude comparative de la distribution orale dansune portion des Alpes et des Jura". In Bulletin del la Socit Vaudoise des Sciences Naturelles, volume 37, pages 547-579.
Index Terms

Computer Science
Information Sciences

Keywords

Type-I fuzzy Type-II fuzzy ultrafuzziness

Powered by PhDFocusTM