Image Segmentation Techniques for Noisy Digital Images based upon Fuzzy Logic- A Review and Comparison
Автор: Prabhjot Kaur, Nimmi Chhabra
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 7 vol.4, 2012 года.
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This paper presents a comparison of the three fuzzy based image segmentation methods namely Fuzzy C-Means (FCM), TYPE-II Fuzzy C-Means (T2FCM), and Intuitionistic Fuzzy C-Means (IFCM) for digital images with varied levels of noise. Apart from qualitative performance, the paper also presents quantitative analysis of these three algorithms using four validity functions-Partition coefficient (V_pc), Partition entropy (V_pe), Fukuyama-Sugeno (V_fs), and Xie-Beni (V_xb) functions and also compared the performance on the basis of their execution time.
Fuzzy Clustering, Fuzzy C-Means, Robust Image Segmentation, FCM TYPE-II, Intuitionistic FCM
Короткий адрес: https://sciup.org/15010277
IDR: 15010277
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