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]
- S.A. Hojjatoleslami, and J. Kittler, “Region growing: a new approach,” IEEE Trans. Image Process., vol. 7, no. 7, pp. 1079–1084, 1998. [CrossRef]
- 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]
- 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]
- 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]
- 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]
- R. Krishnapuram, J.M. Keller, “A possibilistic approach to clustering,” IEEE Trans. Fuzzy Syst., vol.1, no. 2, pp. 98–110, May 1993. [CrossRef]
- 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]
- 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]
- M. S. Yang, K. L. Wu, “Unsupervised Possibilistic Clustering,” Pattern Recognit., vol.39, no.1, pp. 5–21, 2006. [CrossRef]
- 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.
- 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]
- 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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.