Yang Xiao, Chen Xiaobo, Xi Juntong
Opt Express. 2022 Jan 17;30(2):2310-2325. doi: 10.1364/OE.448445.
Despite camera calibration methods using regular planar chessboard or circular marker array calibration targets having been widely used, the control point extraction accuracy is low if the image is defocused or if the noise level is high. Due to the noise robustness of digital image correlation (DIC) in speckle image matching, random speckle pattern is a better choice for camera calibration than chessboard or circular markers, if the imaging quality is low. The foremost process of this method is to conduct speckle control points matching DIC, where the initial value must be estimated close to the true value. It is challenging to provide accurate initial values for DIC if the difference of physical pixel scale is large between the reference image and the target image or if the target image is out-of-focus. To solve this problem, this work presents an efficient initial value estimation method for speckle control points matching using DIC, based on perspective transformation. Firstly, the four pairs of corners of the speckle regions in the reference image and target image are detected. Secondly, the target image is transformed to a new image that has the considerable size of pixel scale with the reference image, then four neighborhood points of the control point in the reference image and the corresponding points in the transformed new image are matched coarsely by fixed subset searching. Lastly, the matched points in the transformed target image are transformed back to the origin target image by the inverse perspective transformation matrix, then the initial value for DIC can be estimated by the matched four pairs of neighborhood points. Experiment results confirm the higher calibration accuracy delivered by the proposed method, rather than that of the chessboard or the circular marker array. Measurement precision is higher than the speckle pattern calibration method that uses SIFT-based initial value estimation.
尽管使用常规平面棋盘或圆形标记阵列校准目标的相机校准方法已被广泛应用，但如果图像散焦或噪声水平较高，控制点提取精度会很低。由于数字图像相关（DIC）在散斑图像匹配中具有噪声鲁棒性，因此在成像质量较低时，随机散斑图案比棋盘或圆形标记更适合用于相机校准。该方法的首要步骤是进行散斑控制点的DIC匹配，其中初始值必须估计得接近真实值。如果参考图像和目标图像之间的物理像素尺度差异很大，或者目标图像失焦，为DIC提供准确的初始值具有挑战性。为了解决这个问题，本文提出了一种基于透视变换的高效初始值估计方法，用于使用DIC的散斑控制点匹配。首先，检测参考图像和目标图像中散斑区域的四对角点。其次，将目标图像变换为与参考图像具有相当像素尺度大小的新图像，然后通过固定子集搜索粗略匹配参考图像中控制点的四个邻域点和变换后的新图像中的对应点。最后，通过逆透视变换矩阵将变换后的目标图像中的匹配点变换回原始目标图像，然后可以通过匹配的四对邻域点估计DIC的初始值。实验结果证实了该方法比棋盘或圆形标记阵列具有更高的校准精度。测量精度高于使用基于尺度不变特征变换（SIFT）的初始值估计的散斑图案校准方法。
