期刊文献+

一种粗糙熵和K-均值聚类相结合的岩心图像分割方法 被引量:2

Rock Image Segmentation Based on Rough Entropyand K-Means Clustering Algorithm
下载PDF
导出
摘要 K-均值聚类算法和粗糙熵是应用于图像分割的主要算法,目的是对图像进行分析处理。将K-均值聚类算法和粗糙熵结合起来应用到岩心图像的分割,目的是提取出岩石的隙缝信息。先利用K-均值聚类算法对岩心图像进行区域分割,再利用基于粗糙熵的方法对分割结果进行目标提取,从而达到多阈值分割的目的。通过效果图对比分析可以看出,采用基于粗糙熵的K-均值聚类算法处理多目标的岩心图像,提取出的目标更清晰,更明确,实验结果更有价值,证明了改进后算法的有效性。 K-means clustering algorithm and rough entropy were mainly used to segment the image for analyzing the image.K-means clustering algorithm and rough entropy were combined to segment the image of rock,and gain the information of the rock rift.K-means clustering algorithm was used to segment the image then the algorithm based on rough entropy was used to extract objects from the segmentation results,so as to achieve multi-threshold segmentation.By contrasting the final result,it shows that the effect of using K-means clustering algorithm based on rough entropy to segment the multi-object rock image is better than other methods,and the result is clear and more valuable.It means that the ameliorated method is more valid.
作者 叶青 周云才
出处 《长江大学学报(自科版)(上旬)》 CAS 2008年第1期68-71,共4页 JOURNAL OF YANGTZE UNIVERSITY (NATURAL SCIENCE EDITION) SCI & ENG
关键词 粗糙熵 K-均值聚类 岩心图像 图像分割 rough entropy K-means clustering rock image image segment
  • 相关文献

参考文献1

二级参考文献2

共引文献4

同被引文献28

引证文献2

二级引证文献6

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部