An Adaptive Fuzzy Entropy Algorithm in Image Edge Detection

被引：0

作者：

Zhou Jihong ^{[1
]}

Lu Jun ^{[2
]}

Ling Xianqing ^{[2
]}

机构：

[1] Yangtze Univ, Sch Geophys & Petr Resource, Jinzhou, Hubei, Peoples R China

[2] North China Elect Power Univ, Sch Elect & Elect Engn, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE 2012 SECOND INTERNATIONAL CONFERENCE ON INSTRUMENTATION & MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC 2012) | 2012年

关键词：

edge detection; fuzzy entropy; measure information; weighted average;

D O I：

10.1109/IMCCC.2012.91

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

To improve the effectiveness of image edge detection, this paper proposed a weighted image edge detection algorithm based on fuzzy entropy. The scheme firstly defines three image metrics to represent image edge: orderliness measure, directivity measure and structural measure. Then it extracts these metrics from images based on fuzzy entropy, and weights three metrics to calculate a confidence degree for edge decision, in which the weighted factors may fully consider the metric impact to detect edges. Finally the algorithm adopts non-maximal image suppression with dynamic threshold to inhibit image pseudo-edge for further optimization. Experiment results illustrate better performance in edge detection with maintaining more image details.

引用

页码：371 / 374

页数：4