数字图像相关中的散斑区域自动提取研究

胡慧然; 但西佐; 赵琪涵; 孙方圆; 王永红

doi:10.3788/CO.20191206.1329

数字图像相关中的散斑区域自动提取研究

doi: 10.3788/CO.20191206.1329

cstr: 32171.14.CO.20191206.1329

合肥工业大学仪器科学与光电工程学院, 安徽合肥 230009

基金项目:

国家重点研发计划 2016YFF0101803

国家自然科学基金资助项目 51805137

详细信息

作者简介:
胡慧然(1995—), 男, 安徽黄山人, 硕士研究生, 2017年于合肥工业大学获得学士学位, 主要从事数字图像相关及图像处理等方面的研究。E-mail:18605592115@163.com

王永红(1972—), 男, 安徽合肥人, 教授, 博士生导师, 主要从事光学精密测试、激光散斑干涉检测和机器视觉等方面的研究。E-mail:yhwang@hfut.edu.cn

中图分类号: TP391.4;TH74
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出版历程
- 收稿日期: 2018-12-12
- 修回日期: 2019-02-01
- 刊出日期: 2019-12-01

Automatic extraction of speckle area in digital image correlation

School of Instrument Science and Optoelectronic Engineering, Hefei University of Technology, Hefei 230009, China

Funds:

National Key Research and Development Program of China 2016YFF0101803

National Natural Science Foundation of China 51805137

More Information

Corresponding author: WANG Yong-hong, E-mail:yhwang@hfut.edu.cn

摘要

摘要: 数字图像相关测量中，相关计算前会人工选取散斑区域进行区域限定。随着工业自动化的发展，面对散斑区域形状越来越复杂以及大量散斑图片的测量需求，找到一种散斑区域自动提取方法至关重要。本文根据散斑的特征，对比多种常规边缘检测方法，提出了一种基于二阶梯度熵函数的散斑区域自动提取判定函数，并通过分析不同的散斑图片，确定了最佳子区熵尺寸区间以及在不同散斑图中的自适应阈值区间，最终通过连通区域分割完成对散斑区域的自动提取。文中采用实际拍摄的散斑图对该方法进行验证，实验结果表明：子区熵尺寸取10 pixel以上，该算法对散斑区域表现敏感；自适应阈值取图中最大梯度熵值的Q-1.25至Q范围内时，可以将散斑区域与背景区域有效分割。基本能完成对散斑区域的自动提取，达到了相关计算前散斑区域选择的目的。
- 数字图像相关 /
- 散斑 /
- 二阶梯度熵 /
- 自适应阈值
Abstract: In digital image correlation measurements, the speckle area is manually selected before the correlation calculation is performed to define the matching area. With the development of industrial automation, facing the complex shape of the speckle area and the need to measure a large number of speckle images, it is crucial to find an automatic area extraction method. According to the characteristics of speckles and by comparing various conventional edge detection methods, a decision function based on second-order gradient entropy is proposed for automatically detecting speckle regions in images. By analyzing different speckle images, the optimal sub-region entropy size interval and the adaptive threshold interval in different speckle patterns were determined and the automatic extraction of the speckle region were completed by using connected region segmentation. The method was verified by using the actual speckle pattern. The experimental results show that when the entropy size of the subregion is more than 10 pixel, the decision function is sensitive to the speckle area. When the adaptive threshold value is within the range of Q-1.25 to Q, the speckle area and the background area can be effectively separated. The automatic extraction of a speckle area can be completed and the selection of speckle area before correlation calculation is achieved.
- digital image correlation /
- speckle /
- second-order gradient entropy /
- adaptive threshold

HTML全文

图 1 几种边缘检测算子处理结果对比

Figure 1. Comparison of processing results from several edge detection operators

下载: 全尺寸图片幻灯片

图 2 拉式变换后散斑区域与背景区域灰度直方图的对比

Figure 2. Comparison of gray histograms between speckle area and background area after laplacian transformation

下载: 全尺寸图片幻灯片

图 3 二阶梯度熵值分割结果

Figure 3. Second order gradient entropy segmentation results

下载: 全尺寸图片幻灯片

图 4 散斑区域提取结果

Figure 4. Extraction results of speckle area

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图 5 散斑区域提取算法的实现流程图

Figure 5. Implementation flow chart for speckle area extraction algorithm

下载: 全尺寸图片幻灯片

图 6 不同熵值随判定子区大小的变化

Figure 6. Different entropy values vary with the size of the decision sub-area

下载: 全尺寸图片幻灯片

图 7 不同样式散斑图像

Figure 7. Different types of speckle images

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图 8 不同的散斑图像

Figure 8. Different speckle images

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图 9 图 8中各散斑图像对应的散斑提取结果

Figure 9. Speckle extraction results corresponding to Fig. 8

下载: 全尺寸图片幻灯片

图 10 散斑区域与背景区域对比验证结果

Figure 10. Comparison results of speckle area and background area

下载: 全尺寸图片幻灯片

表 1 不同样式散斑图对应的二阶梯度熵值

Table 1. Second order entropy values corresponding to different types of speckle patterns

Picture	Max(Q)	Min(Q)	Avg(Q)	Diff(Q)
Pic(a)	6.868 10	5.502 54	6.396 67	1.365 56
Pic(b)	5.218 34	3.965 52	4.793 77	1.252 82
Pic(c)	5.583 25	4.391 42	5.018 05	1.191 83
Pic(d)	4.746 89	3.782 02	4.348 52	0.964 87
Pic(e)	6.368 67	4.974 82	5.945 93	1.393 84
Avg	5.757 05	4.523 26	5.300 59	1.233 78

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