文章摘要
邱丽娟,宣征南,张兴芳.基于K-means聚类与最大类间方差的磨粒彩色图像分割[J].润滑与密封,2014,39(12):101-104
基于K-means聚类与最大类间方差的磨粒彩色图像分割
  
DOI:10.3969/j.issn.0254-0150.2014.12.021
中文关键词: K-means聚类  Lab颜色空间  最大类间方差法
英文关键词: K-means clustering  Lab color space  Ostu method
基金项目:茂名市科技计划项目(201327);广东省石化装备故障诊断重点实验室开放基金项目(GDUPTKLAB201314);广东省高校石油化工过程装备故障诊断与信息化控制工程技术开发中心开放基金项目(512015).
作者单位E-mail
邱丽娟 太原理工大学广东石油化工学院机电工程学院 lijuanth0809@163.com 
宣征南 广东石油化工学院机电工程学院广东省石化装备故障诊断重点实验室  
张兴芳 广东省石化装备故障诊断重点实验室  
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中文摘要:
      针对在HSI颜色空间下存在的图像的二维颜色分量分布散乱不紧密,存在聚类中心计算错误,利用二维颜色分量很难将背景和磨粒准确分割开,分割完的铁谱图像仍包含许多不需要的微小磨粒等问题,提出采用K-means聚类与最大类间方差的图像分割方法。分别选取球粒、切削磨粒、严重滑动磨粒、红色氧化物、疲劳磨粒的彩色图像,在Lab颜色空间下利用二维颜色分量进行聚类分析及最大类间方差阈值分割,并进行三维数学形态学处理。结果表明,提出的方法实现了小磨粒与目标磨粒的有效分割,可以得到较为完整的彩色磨粒图像,为磨粒的颜色参数识别提供有效的依据。
英文摘要:
      The images under the HSI color space distribution of two dimensional color component scattered were not tight and existing clustering center calculation error,it was difficult to accurately separate background and debris using two dimensional color component,and the segmented ferrographic images still contained many tiny debris which were not needed. The image segmentation by K-means Clustering and the between-cluster variance method was put forward. Spherical particles,cutting particles,severe sliding particles,red oxide particles,fatigue particles were selected to segment by clustering and Ostu method under Lab color space’s two dimensional color component,and segmented images were dealt with three-dimensional mathematical morphology method. The results show that the proposed method can achieve effective segmentation between tiny debris and target debris,and can obtain a more complete color wear particle images,it provides effective basis for identification of the color parameters of debris.
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