Infrared image segmentation using Enhanced Fuzzy C-means clustering for automatic detection systems

2011 ◽  
Author(s):  
Sitanshu Gupta ◽  
Asim Mukherjee
Keyword(s):  
Image Segmentation ◽  
Infrared Image ◽  
Automatic Detection ◽  
Fuzzy C Means ◽  
Detection Systems ◽  
Fuzzy C Means Clustering ◽  
Infrared Image Segmentation

Infrared image segmentation based on fast fuzzy C-means clustering

2011 ◽  
Vol 23 (6) ◽  
pp. 1467-1470 ◽  
Author(s):  
黄永林 Huang Yonglin ◽  
叶玉堂 Ye Yutang ◽  
乔闹生 Qiao Naosheng ◽  
陈镇龙 Chen Zhenlong
Keyword(s):  
Image Segmentation ◽  
Infrared Image ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Infrared Image Segmentation

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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