Symmetry incorporated Fuzzy C-means Method for Image Segmentation

被引：8

作者：

Jayasuriya, Surani Anuradha ^{[1
]}

Liew, Alan Wee-Chung ^{[1
]}

机构：

[1] Griffith Univ, Sch Informat & Commun Technol, Gold Coast Campus, Southport, Qld 4222, Australia

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ - IEEE 2013) | 2013年

关键词：

Fuzzy C-means; bilateral symmetry; image segmentation; ALGORITHM;

D O I：

10.1109/FUZZ-IEEE.2013.6622511

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a new modified fuzzy c-means (FCM) clustering algorithm that exploits bilateral symmetry information in image data. With the assumption of pixels that are located symmetrically tend to have similar intensity values; we compute the degree of symmetry for each pixel with respect to a global symmetry axis of the image. This information is integrated into the objective function of the standard FCM algorithm. Experimental results show the effectiveness of the approach. The method was further improved using neighbourhood information, and was compared with conventional fuzzy c-means algorithms.

引用

页数：7

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