College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China.
College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China.
Micron. 2016 Apr;83:93-109. doi: 10.1016/j.micron.2016.01.005. Epub 2016 Feb 2.
We present a novel and high-precision microscopic vision modeling method, which can be used for 3D data reconstruction in micro-gripping system with stereo light microscope. This method consists of four parts: image distortion correction, disparity distortion correction, initial vision model and residual compensation model. First, the method of image distortion correction is proposed. Image data required by image distortion correction comes from stereo images of calibration sample. The geometric features of image distortions can be predicted though the shape deformation of lines constructed by grid points in stereo images. Linear and polynomial fitting methods are applied to correct image distortions. Second, shape deformation features of disparity distribution are discussed. The method of disparity distortion correction is proposed. Polynomial fitting method is applied to correct disparity distortion. Third, a microscopic vision model is derived, which consists of two models, i.e., initial vision model and residual compensation model. We derive initial vision model by the analysis of direct mapping relationship between object and image points. Residual compensation model is derived based on the residual analysis of initial vision model. The results show that with maximum reconstruction distance of 4.1mm in X direction, 2.9mm in Y direction and 2.25mm in Z direction, our model achieves a precision of 0.01mm in X and Y directions and 0.015mm in Z direction. Comparison of our model with traditional pinhole camera model shows that two kinds of models have a similar reconstruction precision of X coordinates. However, traditional pinhole camera model has a lower precision of Y and Z coordinates than our model. The method proposed in this paper is very helpful for the micro-gripping system based on SLM microscopic vision.
我们提出了一种新颖且高精度的微观视觉建模方法，可用于具有立体光显微镜的微夹持系统中的 3D 数据重建。该方法由四个部分组成：图像失真校正、视差失真校正、初始视觉模型和残差补偿模型。首先，提出了图像失真校正方法。图像失真校正所需的图像数据来自校准样本的立体图像。可以通过构建在立体图像中的网格点的线的形状变形来预测图像失真的几何特征。应用线性和多项式拟合方法来校正图像失真。其次，讨论了视差分布的形状变形特征。提出了视差失真校正方法。应用多项式拟合方法来校正视差失真。第三，推导了微观视觉模型，它由两个模型组成，即初始视觉模型和残差补偿模型。通过分析物体和图像点之间的直接映射关系，我们推导出初始视觉模型。基于初始视觉模型的残差分析，推导出残差补偿模型。结果表明，在 X 方向的最大重建距离为 4.1mm、Y 方向为 2.9mm 和 Z 方向为 2.25mm 的情况下，我们的模型在 X 和 Y 方向上的精度达到 0.01mm，在 Z 方向上的精度达到 0.015mm。将我们的模型与传统针孔相机模型进行比较表明，两种模型在 X 坐标的重建精度上具有相似性。然而，传统针孔相机模型在 Y 和 Z 坐标上的精度低于我们的模型。本文提出的方法对基于 SLM 微观视觉的微夹持系统非常有帮助。
