International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us

IJSTR >> Volume 1 - Issue 8, September 2012 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Image Segmentation Based On FUZZY GLSC Histogram With Dynamic Similarity Discrimination Factor

[Full Text]



N. Swathi, K. Ravi Kumar



Keywords :- Entropy, Fuzzyfication, Fuzzyfied image, GLSC histogram, threshold.



Abstract:- Image pressing applications performes image segmentation as pre-processing technique to extract the features for next stage. The application performance depends on image segmentation, to process the foreground or background objects. The image segmentation plays a vital role in computer vision and image processing applications. Inspite of having many thresholding techniques in literature they have their own limitations. This paper proposes a new method of thresholding using Gray Level Spatial Correlation (GLSC) histogram with a dynamic similarity discrimination factor( ) and Fuzzy logic in deciding the threshold using Shannon\'s entropy. The similarity discrimination factor( ) is made dynamic by considering the absolute difference between the global and local mean of the image. Calculating the threshold in the fuzzyfied region makes the segmentation process the most time efficient than the existing methods. Experimental results proove better efficiency than the existing methods. The technique out performs in case of low contrast images.



1. R. C. Gonzalez and R. E.Woods, Digital Image Processing. Reading, MA: Addison-Wesley, 1993.

2. M . Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative p erformance evalu ation,” J.Electron. Imag., vol. 13, no. 1, pp. 146–165, Jan. 2004.

3. N. R. Pal and S. K. Pal, “A review on image segmentation techniques”, p attern recog.,vol.26,No. 9, pp.1277-1294,1993.

4. N. Otsu, “A threshold selection method from gray level histograms,” IEEE Trans. Syst., Man, Cybern., vol. SMC-9, pp. 62–66, 1979

5. C. E.Sh annon,”A mathematical theory of communications”, Bell. Syst.,tech. pp.623-656,J.27,1948

6. T. Pun, “A new method for gr ay -level picture thresholding using the entrop y of the histogram,” Signal Process., vol. 2, no. 3, pp. 223–237,1980.

7. J. N. Kap ur, P. K. Sahoo, and A. K. C.Won g, “A new method for graylevel picture thresholding using the entropy of the histogram,” Graph. Models Image Process., vol. 29, pp. 273–285, 1985.

8. P.K. Sahoo, and G. Arora., “A thresholdin g method based on two-dimensional Reny i‟s entropy ”, Pattern Recognit.,2004, pp. 1149-1161.

9. Sahoo, P., Willkins, C., and Yeager, J., “Threshold selection using Reny i‟s entropy ”, Pattern Recognit., 1997, pp. 71-84.

10. P.K. Sahoo, and G. Arora, “Image thresholding using twodimensional Tsallis-Havrda-Charvát entropy ”, Pattern Recognit. Lett., 2006, pp. 520-528.

11. Portes de Albuquerque, M., Esquef, I. A., et al., “Image thresholding using Tsallis entropy”, Pattern Recognit. Lett.,2004 , pp. 1059-1065.

12. Yang Xiao, Zhiguo Cao, Tian xu Zhang “Entropic thresholding based on gray level spatial correlation histogram”,I EEE trans. 19th international conf., pp. 1-4,ICPR-2008,

13. A.S. Abutaleb, “Automatic thresholding of gray –level picture using two-dimensional entropies”, Pattern Recognit., 1989 pp.22-32.

14. A Kaufinnann, Introduction to the Theory ofFuzzy Subsets – Fundamental Theoretical Elements. vol. 1, New York Academic Press, 1975.

15. E.Pasha, R. Farnoosh, A.Fatemi,” Fuzzy Entropy as Cost Function in Image Processing”,proceedings of the 2nd IMT –GT Regional conference on Mathematics, Statistics and Applications, Universiti Sains Malaysia, Penang, june 13-15,2006.