主管:中华人民共和国应急管理部
主办:应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU

消防科学与技术 ›› 2020, Vol. 39 ›› Issue (1): 27-30.

• • 上一篇    下一篇

金相图像分割和特征提取方法

罗序擎,邓亮   

  1. 中国人民警察大学,河北廊坊06500
  • 收稿日期:2019-09-22 出版日期:2020-01-15 发布日期:2020-09-17
  • 通讯作者: 邓亮(1977-),男,中国人民警察大学火灾调查教研室副教授。
  • 作者简介:罗序擎(1994-),男,重庆渝中人,中国人民警察大学研究生一队在读硕士研究生,主要从事火灾调查及物证鉴定研究,河北省廊坊市西昌路220 号,065000。
  • 基金资助:
    国家科学基础性专项“我国火灾调查与相关工作数据库研建”(SQ2015FY3120051)

Research on metallographic image segmentationand feature extraction

LUO Xu-qing, DENG Liang   

  1. China People's Police University, Hebei Langfang 065000, China)
  • Received:2019-09-22 Online:2020-01-15 Published:2020-09-17

摘要: 为了实现对熔痕金相的有效晶格自动划分与相应的内部特征信息描述,针对金相图像边界模糊、内部晶粒块混乱、晶粒形态分布不规则、切削表面图像噪声大的特点,研究了适合于金相图像分割的图像分割算法和特征提取算法。根据鉴定工作中的实际情况,对熔痕金相进行了基本的直方图处理。在直方图规定化的基础上,对金相进行边缘检测算子的比较和图像形态学重建,设计并改进分水岭算法进行图像分割。为了获得金相图像的方向性梯度特征,采取HOG 算法进行计算。实验结果表明:经过直方图处理,并采取改进后的分水岭算法和HOG 算法,能有效进行晶格区域划分和内部特征信息提取。

关键词: 熔痕金相, 直方图规定化, 边缘检测, 图像分割, HOG算法, 火灾调查

Abstract: In order to achieve the automatic lattice division of themolten copper conductors metallography and corresponding internal feature information description, according to the characteristicsof the metallographic image, such as the not obvious boundary, the confusing internal grain blocks, the irregular grain shape, and the heavy noise of cut surface, the image segmentation algorithm and the feature extraction algorithm fit for the metallography are studied. First, according to the actual situation in the identification work, the basic histogram processing of the molten metallography was performed. Then, based on the histogram specification, compared the edge detection operators and reconstructed the image morphology, and then designed and improved the watershed algorithm for automatic lattice division. Finally, the directional gradient feature of the metallographic image is obtained by the HOG algorithm. The experimental results showed that after the histogram processing and the improved watershed algorithm and HOG algorithm, the lattice area division and internal feature information extraction can be effectively performed.

Key words: elt metallography, histogram specification, edge detection, image segmentation, HOG algorithm;fire investigation