Open Access
Issue
MATEC Web of Conferences
Volume 56, 2016
2016 8th International Conference on Computer and Automation Engineering (ICCAE 2016)
Article Number 02002
Number of page(s) 5
Section Image Processing and Application
DOI https://doi.org/10.1051/matecconf/20165602002
Published online 26 April 2016
  1. M.O. Baradez, C.P. McGuckinb, N. Forrazb, R. Pettengell, and A. Hoppe, “Robust and automated unimodal histogram thresholding and potential applications,” Pattern Recognit., vol. 37, no. 6, pp. 1131-1148, 2004. [CrossRef] [Google Scholar]
  2. S.A. Hojjatoleslami, and J. Kittler, “Region growing: a new approach,” IEEE Trans. Image Process., vol. 7, no. 7, pp. 1079–1084, 1998. [CrossRef] [Google Scholar]
  3. B. Caldairoua, N. Passata, P.A. Habas, C. Studholme, and F. Rousseau, “A non-local fuzzy segmentation method: application to brain MRI,” Pattern Recognit., vol. 44 no.9, pp. 1916–1927, 2010. [CrossRef] [Google Scholar]
  4. B. Sowmya, and B.S. Rani, “Color image segmentation using fuzzy clustering techniques and competitive neural network,” Appl. Soft Comput., vol.11 no.3, pp. 3170–3178, 2011. [CrossRef] [Google Scholar]
  5. W.L. Cai, S.C. Chen, and D.Q. Zhang, “Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation,” Pattern Recognit., vol.40 no.3, pp. 825–838, 2007. [CrossRef] [Google Scholar]
  6. M. G. Gong, Y. Liang, J. Shi, W.P. Ma, and J.J. Ma, “Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation,” IEEE Trans. Image Process., vol. 22, no. 2, pp. 573–584, Feb. 2013. [CrossRef] [Google Scholar]
  7. R. Krishnapuram, J.M. Keller, “A possibilistic approach to clustering,” IEEE Trans. Fuzzy Syst., vol.1, no. 2, pp. 98–110, May 1993. [CrossRef] [Google Scholar]
  8. R. Krishnapuram, J.M. Keller, “The possibilistic Cmeans algorithm: insights and recommendations,” IEEE Trans. Fuzzy Syst., vol.4, no.3, pp. 385–393, Aug 1996. [CrossRef] [Google Scholar]
  9. M. Barni, V. Cappellini, and A. Mecocci, “Comments on ‘A Possibilistic Approach to Clustering,‘” IEEE Trans. Fuzzy Syst., vol. 4, pp. 393–396, Aug. 1996. [CrossRef] [Google Scholar]
  10. M. S. Yang, K. L. Wu, “Unsupervised Possibilistic Clustering,” Pattern Recognit., vol.39, no.1, pp. 5–21, 2006. [CrossRef] [Google Scholar]
  11. Y.F. Xu, “Image Segmentation Based on the Genetic Fuzzy C-Mean Algorithm,” Journal of Northwestern Polytechnical University, vol.20, no.4, pp. 549–553, November, 2002. [Google Scholar]
  12. K.S. Chuang, H.L. Tzeng, S. Chen, J. Wu, and T.J. Chen, “Fuzzy c-means clustering with spatial information for image segmentation,” Computerized Medical Imaging and Graphics, vol. 30, no. 1, pp. 9–15, 2006. [CrossRef] [Google Scholar]
  13. H. Zuo, and W. Li, “Improved PCM Clustering Algorithm and Its Application in Image Segmentation,” Computer & Digital Engineering, vol.38, no.11, pp.148–151, June 2010. [Google Scholar]

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