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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。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.
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Key words:
- digital image correlation /
- speckle /
- second-order gradient entropy /
- adaptive threshold
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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
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
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
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
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
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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