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建立成像过程的能力对于视觉测量至关重要.非参数相机模型将图像形成过程描述为与来自物体的空间射线对应的像素集合.然而,非参数模型需要复杂的计算或高成本的装置来获取大量参数,这限制了该模型的应用.为此,提出了一种基于单轴旋转靶标的非参数成像模型标定方法.旋转靶标提供三维控制点,并引入径向基神经网络将三维坐标映射到二维图像坐标.该方法无需旋转靶标的详细位姿信息,从而避免了额外的角度测量设备.随后,推导出成像模型参数的目标函数,给出了优化步骤,进而计算出模型参数.在获得单个相机中的光线轨迹后,该模型被迁移于双目立体相机应用.最后,通过实验对所提出方法的性能进行了评估,结果表明,与传统方法相比该方法优势明显.“,”The ability to build an imaging process is crucial to vision measurement.The non-parametric imaging model describes an imaging process as a pixel cluster,in which each pixel is related to a spatial ray originated from an object point.However,a non-parametric model requires a sophisticated calculation process or high-cost devices to obtain a massive quantity of parameters.These disadvantages limit the application of camera models.Therefore,we propose a novel camera model calibration method based on a single-axis rotational target.The rotational vision target offers 3D control points with no need for detailed information of poses of the rotational target.Radial basis function(RBF)network is introduced to map 3D coordinates to 2D image coordinates.We subsequently derive the optimization formulization of imaging model parameters and compute the parameter from the given control points.The model is extended to adapt the stereo camera that is widely used in vision measurement.Experiments have been done to evaluate the performance of the proposed camera calibration method.The results show that the proposed method has superiority in accuracy and effectiveness in comparison with the traditional methods.