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Image Segmentation Techniques for Noisy Digital Images based upon Fuzzy Logic- A Review and Comparison

机译:基于模糊逻辑的噪声数字图像图像分割技术-综述与比较

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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.
机译:本文介绍了三种基于模糊的图像分割方法的比较,这些方法分别是具有变化噪声水平的数字图像的模糊C均值(FCM),TYPE-II模糊C均值(T2FCM)和直觉模糊C均值(IFCM)。 。除了定性性能外,本文还使用四个有效性函数(分区系数(V_pc),分区熵(V_pe),福山-杉野(V_fs)和谢贝尼(V_xb)函数)对这三种算法进行了定量分析,并进行了比较基于执行时间的性能。

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