On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system
On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system
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DOI：
10.1109/97.720555
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发表时间：
1998-10-01
影响因子：
3.9
通讯作者：
Panas, SM
中科院分区：
文献类型：
--
作者：
Tolias, YA;Panas, SM
在这封信中，提出了一种新的方法来提高模糊聚类的结果，通过施加空间约束来解决图像分割问题。我们已经开发了一个Sugeno型基于规则的系统[9]，该系统具有3个输入和11个规则，这些规则与由众所周知的FCM和/或PCM算法获得的聚类结果相互作用。它提供了良好的图像分割的区域平滑度和消除噪声的影响。将所提出的基于规则的邻域增强（RB-NE）系统的结果与使用随机场建模的众所周知的分割算法进行比较。他们被发现是相当的质量，同时具有较低的计算复杂度。
In this letter, a novel approach for enhancing the results of fuzzy clustering by imposing spatial constraints for solving image segmentation problems is presented. We have developed a Sugeno-type rule-based system [9] with three inputs and 11 rules that interacts with the clustering results obtained by the well-known FCM and/or PCM algorithms. It provides good image segmentations in terms of region smoothness and elimination of the effects of noise. The results of the proposed rule-based neighborhood enhancement (RB-NE) system are compared to well-known segmentation algorithms using stochastic field modeling. They are found to be of comparable quality, while being of lower computational complexity.