基于BP神经网络的线性结构光系统摄像机标定

Li Fu, Zhenzhong Liu, Baorui Du
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引用次数: 6

摘要

本文将BP神经网络算法应用于线性结构光三维数字测量系统的摄像机标定,基于Levenberg-Marquardt算法(LM算法)建立了双输入双输出摄像机标定的BP网络模型。仿真试验验证了从三维运动平台获取的特征点数据,结果表明,双输入双输出的BP网络在非线性函数的泛化和逼近能力方面具有优势,在线性结构光三维测量系统的标定中具有较高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Camera calibration for linear structured light system based on BP neural network
In this paper, BP neural network algorithm is applied to camera calibration for the linear structured light 3D digital measurement system and BP network model of camera calibration with double-input double-output is established based on the Levenberg-Marquardt algorithm (LM algorithm). The simulation tests which verify the data of characteristic points obtaining from 3D motion platform show that BP network with double-input and double output has advantages in terms of generalization and approximation ability of nonlinear function, and high accuracy in calibrating linear structured light 3D measurement system.
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