摘要
提出了一种结合数据挖掘方法——聚类分析提取图像边缘的算法.该算法先用拉普拉斯算子给出图像各像素的二阶导数,然后根据一组预先给定的阈值,将图像中的像素分类,每一个像素仅属于一类.在这些类中进行某种准则的凝聚层次聚类.完成聚类后,就得到了图像的边界.比较而言,该算法有效地避开了由噪声信号引起的大量孤立点,更清楚地输出图像边界.在一定程度上,该算法弥补了拉普拉斯算子的不足(孤立点比较多),发扬了拉普拉斯算子的长处(边界完整程度比较高).
A algorithm of image edge detection with data clustering , a method of data mining , is proposed in this paper . In an image , after having calculated the differential coefficient values of the pixels by Laplacian operator , we make the pixels into some categorys according to the differential coefficient values of the pixels . Then make data clustering in the categorys in some criterions . When the process of the clustering is finished , we get the edge of the image . The advantage of the algorithm is that the algorithm can reject a large number of noises and get the more clear edge of the image .
出处
《广西民族大学学报（自然科学版）》
CAS
2008年第4期83-86,102,共5页
Journal of Guangxi Minzu University ：Natural Science Edition
关键词
数据挖掘
聚类
图像处理
边缘检测
Data Mining
Cluster
image procession
Edge Detection
