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Application of gradient-based Hough transform to the detection of corrosion pits in optical images

机译：基于梯度的霍夫变换在光学图像腐蚀点检测中的应用

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In this paper, we introduce a circle detection technique named Hough transform to automatically recognize the corrosion pits in microscopic images. All the points in the input image are transformed into a parameter space, which is represented by a two-dimensional accumulative array with the same size of the original image. Local extreme values in the accumulative array, which represent the candidates of corrosion pits, are located using a maxima searching algorithm. The accuracy of detecting the number, radius and coordinate of pits from simulated images was examined. The results show that more than 95% of pits were successfully detected and the average errors of radius and coordinate are less than 10%, while these errors have negligible effect on the pit size distribution. The introduced method can also differentiate pits from scratches or inclusions, as indicated by the 100% accuracy of pit detection, from the simulated images presented in this study. Therefore, it is believed that the gradient-based Hough transform is a powerful method for the recognition of corrosion pits in microscopic images, making the statistical analysis of pit size and pit locations easier and more efficient. (C) 2016 Elsevier B.V. All rights reserved.
机译：在本文中，我们引入了一种名为Hough变换的圆检测技术，可以自动识别显微图像中的腐蚀点。输入图像中的所有点都将转换为参数空间，该参数空间由具有与原始图像相同大小的二维累积数组表示。使用最大值搜索算法定位累积数组中代表腐蚀点候选的局部极值。检验了从模拟图像中检测凹坑的数量，半径和坐标的准确性。结果表明，成功检测出95％以上的凹坑，半径和坐标的平均误差小于10％，而这些误差对凹坑尺寸分布的影响可忽略不计。引入的方法还可以从本研究中提供的模拟图像中区分出凹痕与划痕或夹杂物，如凹痕检测的100％精度所示。因此，据信基于梯度的霍夫变换是一种用于在显微图像中识别腐蚀点的有效方法，从而使对坑尺寸和坑位置的统计分析更加容易和有效。 （C）2016 Elsevier B.V.保留所有权利。

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