机器视觉系统中摄像机的神经网络定标

将传统摄影测量定标和神经网络相结合，克服传统摄像机定标中因像差等非线性因素造成稳定性不好且算法复杂的缺点。采用基于神经网络的计算机视觉定标方法，用5步法计算未知点的物方坐标。在传统摄影测量定标方法中，需要先制作定标块，其上布置一些控制点，准确测定它们的三维坐标。为保证定标精度，这些控制点必须在三维空间均匀分布，对定标块的制作和加工要求很高。本文提出采用定标板沿法线方向移动的方式代替定标块，既能达到三维效果，显著增加控制点数量，也使制作容易。实验结果表明，以该模板基于神经网络的摄像机定标方法可以获得很高的精度和稳定性。

Camera Calibration in Computer Vision Based on Artificial Neural Network

We combine the traditional photogrammetry calibration with neural network in this paper to overcome instability and computation complexity due to nonlinearity factors such as aberration.Calibration parameters for computer vision are obtained based on neural network,and the unknown point world coordinates calculated with a five-step method.In traditional photogrammetry calibration,a calibration block is needed,with some control points that require accurate determination of three dimensional coordinates.To achieve high accuracy,these points must be evenly distributed in a three dimensional space,and the coordinates measured with high precision.It is difficult to make the calibration block,especially a big one.We propose to move the calibration plate along its normal direction instead of using a calibration block.The moving plate is a three dimensional block.While achieving a three-dimensional effect,it significantly increases the number of control points.Furthermore,the plate is much easier to make than a three dimensional block.Experimental results show that the proposed neural network based method with moving calibration plate can provide high accuracy and stability.