金相图像的晶界恢复与重建

蒋明星; 陈国华

doi:10.3788/OPE.20111910.2541

您当前的位置：

首页 >

文章列表页 >

金相图像的晶界恢复与重建

信息科学 | 更新时间：2020-08-12

- 金相图像的晶界恢复与重建
- Restoration and reconstruction of grain boundary in metallographical image
- 光学精密工程 2011年19卷第10期页码：2541-2549
- 作者机构：
  
  1. 华南理工大学机械与汽车工程学院, 广东广州 510640
  2. 华南农业大学理学院, 广东广州 510642
- 作者简介：
- 基金信息：
  
  广东省自然科学基金资助项目(No.7006700)()
- DOI：10.3788/OPE.20111910.2541
  中图分类号： TP391.4
- 收稿日期：2011-02-15，
  
  修回日期：2011-03-18，
  
  网络出版日期：2011-10-27，
  
  纸质出版日期：2011-10-25
- 稿件说明：
移动端阅览
蒋明星, 陈国华. 金相图像的晶界恢复与重建[J]. 光学精密工程, 2011,19(10): 2541-2549

JIANG Ming-xing, CHEN Guo-hua . Restoration and reconstruction of grain boundary in metallographical image[J]. Editorial Office of Optics and Precision Engineering, 2011,19(10): 2541-2549
蒋明星, 陈国华. 金相图像的晶界恢复与重建[J]. 光学精密工程, 2011,19(10): 2541-2549 DOI： 10.3788/OPE.20111910.2541.

JIANG Ming-xing, CHEN Guo-hua . Restoration and reconstruction of grain boundary in metallographical image[J]. Editorial Office of Optics and Precision Engineering, 2011,19(10): 2541-2549 DOI： 10.3788/OPE.20111910.2541.

摘要

针对定量金相分析中金相图像的晶界恢复与重建以及数学形态学在图像处理中的特殊作用

从理论上证明了传统膨胀运算对图像灰度连续性的影响及影响程度与结构元素尺寸的关系

并对传统的膨胀运算的定义做了改进

据此提出了多尺度测地膨胀算法并以此恢复和重建金相图像的晶界。首先依据改进的膨胀运算对金相图像进行预处理;然后用多尺度迭代腐蚀和多尺度测地膨胀方法得到晶粒的种子;最后用条件粗化方法对晶粒的种子进行区域生长、产生晶界线。实验结果表明

与传统的重复测地膨胀方法以及基于微分运算的图像分割等方法相比

本方法不仅可快速恢复与重建晶界线

而且获取的晶界线更加清晰准确、连续光滑。

Abstract

According to the restoration and reconstruction of the grain boundaries in a metallographical image and the important role of mathematical morphology in image processing

the influences of traditional dilation of mathematical morphology on the gray-scale continuity of an image was demonstrated theoretically

and the relationship between the degree of influence and the sizes of structural elements was discussed. Then

an improved definition for the traditional dilation was proposed. A multi-scale geodesic dilation algorithm was presented to restore and reconstruct the grain boundaries based on the improved definition of dilation. First

the metallographical image was preprocessed based on the improved definition of dilation. Second

the seeds of the grains in the metallographical image were gotten by the methods of multi-scale iterative erosion and geodesic dilation. Finally

the grain boundaries in the metallographical image were obtained by the algorithm of conditioned dilation. Experimental results show that the proposed algorithm is not only faster than the traditional methods of geodesic dilation in the restoration and reconstruction of the grain boundary

but also can obtain the grain boundaries with more clearness

accuracy

continuity and smoothness.

关键词

Keywords

references

MEBATSION H K,PALIWAL J. A Fourier analysis based algorithm to separate touching kernels in digital images [J]. Biosystems Engineering, 2011,108(1):66-74.[2] LU B B, CUI M, LIU Q, et al.. Automated grain boundary detection using the level set method [J]. Computers and Geoscienes, 2009, 35(2):267-275.[3] 肖春霞,初雨,张青. 高斯混合函数区域匹配引导的Level Set纹理图像分割[J]. 计算机学报,2010,33(7):1295-1304. XIAO CH X, CHU Y, ZHANG Q. Texture image segmentation using level set function evolved by Gaussian mixture model [J]. Chinese Journal of Computers, 2010, 33(7):1295-1304. (in Chinese)[4] OBARA B. A new algorithm using image colour system transformation for rock grain segmentation [J]. Mineralogy and Petrology, 2007, 91:271-285.[5] 汤力琨. 金相组织图像分析中关键算法研究 . 成都: 四川大学, 2005: 28-53. TANG L K. Research of some key algorithms in metallographic image analysis system . Graduate Sichuan University, 2005: 28-53. (in Chinese)[6] 廖正全,滕奇志,罗代升. 合金图像分割算法研究[J]. 计算机应用,2009,29(12):3326-3328. LIAO ZH Q, TENG Q ZH, LUO D SH. Research of metallographic image segnentation algorithm [J]. Journal of Computer Applications, 2009, 29(12): 3326-3328. (in Chinese)[7] 邓仕超,刘铁根,萧泽新. 应用Canny算法和灰度等高线的金相组织封闭边缘提取[J]. 光学精密工程,2010,18(1):2315-2323. DENG SH CH, LIU T G, XIAO Z X. Extraction of enclosing image edge for metallographic structure based on Canny and grayscale contour line [J]. Opt. Precision Eng., 2010,18(18): 2315-2323. (in Chinese)[8] 张俊华, 江源源, 董怡,等. 基于椭圆形状约束的图切割对颈部淋巴结的超声图像分割算法[J]. 航天医学与医学工程, 2008, 21(5):430-434. ZHANG J H, WANG Y Y, DONG Y, et al.. Ultrasonographic segmentation of cervical lymph nodes based on the graph cut with elliptical shape constraint [J]. Space Medicine & Medical Engineering, 2008, 21(5):430-434. (in Chinese)[9] TOLOSA S, BLACHER S, DENIS A, et al.. Two methods of random seed generation to avoid over-segmentation with stochastic watershed: application to nuclear fuel micrographs [J]. Journal of Microscopy-oxford, 2009, 236(1):79-86.[10] 袁天云,姜志国,孟如松. 目标分割图中粘连对象的自动切割和分离[J]. 中国体视学与图像分析,2003,8(1):40-43. YUAN T Y, JIANG ZH G, MENG R S. Automatic splitting and separating overlapped objects in target-segmented image [J]. Chinese Journal of Stereology and Image Analysis, 2003,8(1):40-43. (in Chinese)[11] 李新城,王有鹏,朱伟兴,等. 基于形态学的金相组织图像晶界复原方法[J]. 计算机工程与设计,2008,29(14):3807-3809. LI X CH, WANG Y P, ZHU W X, et al.. Metallurgical microstructure image restoration method based on morphology [J]. Computer Engineering and Design, 2008,29(14):3807-3809. (in Chinese)[12] 刘双科,王建飞. 材料显微组织中的晶界识别与提取[J]. 中国体视学与图像分析, 2008, 13(2):134-137. LIU SH K, WANC J F. Recognition and edge-extraction of grain boundary in m aterials'm icrostructure [J]. Chinese Journal of Stereology and Image Analysis, 2008, 13(2):134-137. (in Chinese)[13] 章毓晋.图像工程(中册)图像分析[M]. 北京:清华大学出版社,2005:367-428. ZhANG Y J. Image Processing(II) Image Analysis [M]. Beijing:Tsinghua University Press, 2005:367-428. (in Chinese)[14] 夏涛,黄士科,陈海清. 基于局部灰度分析的红外图像轮廓跟踪算法[J]. 激光与红外,2006,36(2):151-154. XIA T, HUANG SH K, CHEN H Q. Contour tacking for infrared image based on the characteristic analysis of the local gray level [J]. Laser & Infrared, 2006, 36(2):151-154. (in Chinese)[15] WANG X S, QI D W, LI Y X. Edge detection of decayed wood image based on mathematical morphological double gradient algorithm . 2008 IEEE International Conference on Automation and Logistics, 2008,1(6):1226-1231. [16] JIANG J A, CHUANG C L, LU Y L, et al.. Mathematical-morphology-based edge detectors for detection of thin edges in low-contrast regions [J]. Institution of Engineering and Technology Image Processing, 2007,1(3):269-277.

浏览量

489

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据