摘要
提出了一种新的基于多特征和FCM的边缘检测算法.该方法根据边缘点附近灰度分布特点构造了多个反映边缘特性的特征分量,并利用输入图像提取该组特征分量,组成一个反映图像边缘特征的数据集.用FCM聚类算法将该数据集分为两类,即边缘点数据和非边缘点数据,实现边缘检测.该方法无需确定阈值,对弱边缘检测较敏感,在特征的选取上充分考虑了边缘和噪声的本质区别,因而具有优异的抗噪性能.
An edge detection method of the infrared image based multiple features and the fuzzy C-means algorithm （FCM） is proposed. The multiple edge features are defined according to the character of the gray intensity distribution at the image edge. An vector is composed by the multiple edge features, then the vectors of all pixels in input image are worked out, and compose a data set that can reflect the natural characters of image edge, then divide the data set into two clusters of the edge data set and not edge data set by FCM. Our method does not need any threshold; more sensitive for weak edge detection; has better anti-noise performance since the influence of noise is adequately considered when the feature vector is selected.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2005年第12期1893-1896,共4页
Acta Photonica Sinica
基金
国家自然科学基金项目(69972041)
关键词
多边缘特征
边缘检测
FCM
Multiple edge features
Edge detection
Fuzzy C-means algorithm （FCM）
