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首页> 外文会议>Sixth International Conference on Quality Control by Artificial Vision May 19-22, 2003 Gatlinburg, Tennessee, USA >Detection of cavitation pits on steel surfaces using SEM imagery
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Detection of cavitation pits on steel surfaces using SEM imagery

机译：使用SEM图像检测钢表面上的气蚀坑

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摘要

We describe an automated image processing approach for detecting and characterizing cavitation pits on stainless steel surfaces. The image sets to be examined have been captured by a scanning electron microscope (SEM). Each surface region is represented by a pair of SEM images, one captured before and one after the cavitation-causing process. Unfortunately, some required surface preparation steps between pre-cavitation and post-cavitation imaging can introduce artifacts and change image characteristics in such a way as to preclude simple image-to-image differencing. Furthermore, all of the images were manually captured and are subject to rotation and translation alignment errors as well as variations in focus and exposure. In the presented work, we first align the pre- and post-cavitation images using a Fourier-domain technique. Since pre-cavitation images can often contain artifacts that are very similar to pitting, we perform multi-scale pit detection on each pre- and post-cavitation image independently. Coincident regions labeled as pits in both pre- and post-cavitation images are discarded. Pit statistics are exported to a text file for further analysis. In this paper we provide background information, algorithmic details, and show some experimental results.
机译：我们描述了一种自动图像处理方法，用于检测和表征不锈钢表面上的气蚀坑。要检查的图像集已通过扫描电子显微镜（SEM）捕获。每个表面区域均由一对SEM图像表示，其中一个在空化过程之前捕获，一个在其后捕获。不幸的是，在空化之前和空化之后成像之间所需的一些表面准备步骤会引入伪影并以防止简单的图像到图像差异的方式改变图像特性。此外，所有图像都是手动捕获的，并且会发生旋转和平移对齐错误以及焦点和曝光变化。在提出的工作中，我们首先使用傅立叶域技术对齐空化前后的图像。由于空化之前的图像通常可能包含与点蚀非常相似的伪影，因此我们对每个空化前后的图像进行独立的多尺度凹坑检测。在空化之前和之后的图像中标记为凹坑的重合区域都将被丢弃。坑统计信息将导出到文本文件以进行进一步分析。在本文中，我们提供了背景信息，算法细节并显示了一些实验结果。

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