Applying a subpixel refinement technique in vision-based displacement sensing can significantly improve the measurement accuracy. However, digital image signals from the camera are highly sensitive to drastically varying lighting conditions in the field measurements of structural displacement, causing pixels expressing a tracking target to have nonuniform grayscale intensity changes in different recording video frames. Traditional feature points-based subpixel refinement techniques are neither robust nor accurate enough in this case, presenting challenges for accurate measurement. This paper proposes a robust subpixel refinement technique—self-adaptive edge points matching (SEPM)—to obtain accurate subpixel-level displacements under drastic illumination change conditions. Different from traditional feature points-based methods, the gradient and shape information of the target edge contour are used in the SEPM calculation. Three tests under different illumination conditions were conducted to evaluate the performance of the SEPM. The results show that the SEPM is capable of producing accurate subpixel-level displacement data with less 1/16-pixel root mean-square error.

中文翻译：

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一种基于视觉的结构位移测量自适应边缘点匹配的稳健亚像素细化技术

在基于视觉的位移传感中应用亚像素细化技术可以显着提高测量精度。然而，来自相机的数字图像信号对结构位移现场测量中剧烈变化的光照条件高度敏感，导致表示跟踪目标的像素在不同的记录视频帧中具有不均匀的灰度强度变化。在这种情况下，传统的基于特征点的亚像素细化技术既不够稳健也不够准确，对准确测量提出了挑战。本文提出了一种鲁棒的亚像素细化技术——自适应边缘点匹配 (SEPM)——以在剧烈的光照变化条件下获得准确的亚像素级位移。不同于传统的基于特征点的方法，目标边缘轮廓的梯度和形状信息用于SEPM计算。在不同光照条件下进行了三个测试，以评估 SEPM 的性能。结果表明，SEPM 能够生成准确的亚像素级位移数据，且 1/16 像素均方根误差更小。