MATEC Web of Conferences
Volume 56, 20162016 8th International Conference on Computer and Automation Engineering (ICCAE 2016)
|Number of page(s)||5|
|Section||Image Processing and Application|
|Published online||26 April 2016|
- 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]
- S.A. Hojjatoleslami, and J. Kittler, “Region growing: a new approach,” IEEE Trans. Image Process., vol. 7, no. 7, pp. 1079–1084, 1998. [Google Scholar]
- 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]
- 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]
- 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. [Google Scholar]
- 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]
- R. Krishnapuram, J.M. Keller, “A possibilistic approach to clustering,” IEEE Trans. Fuzzy Syst., vol.1, no. 2, pp. 98–110, May 1993. [Google Scholar]
- 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. [Google Scholar]
- 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. [Google Scholar]
- M. S. Yang, K. L. Wu, “Unsupervised Possibilistic Clustering,” Pattern Recognit., vol.39, no.1, pp. 5–21, 2006. [CrossRef] [Google Scholar]
- 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]
- 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]
- 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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