Research of camera calibration based on genetic algorithm BP neural network

Hengfeng Yao, Zhibin Zhang
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引用次数: 9

Abstract

Camera calibration is necessary in machine vision application field. Calibration model has nonlinear characteristics, and establishment of mathematical model is often a complicated process, but neural network can solve the complex nonlinear problem effectively, neural network has strong nonlinear approximation ability, adaptive network parameters and fast learning. This paper presents a neurocalibration approach about camera calibration based on back propagation (BP) neural network optimized by genetic algorithm (GA), GA can optimize net parameters about connection weights and threshold values. Making a comprehensive comparison between GA-BP neural network and BP neural network. The experimental results show that the GA-BP neurocalibration can be effective and feasible by this way.
基于遗传算法BP神经网络的摄像机标定研究
摄像机标定是机器视觉应用领域中必不可少的环节。校正模型具有非线性特性,数学模型的建立往往是一个复杂的过程,而神经网络可以有效地解决复杂的非线性问题,神经网络具有较强的非线性逼近能力、自适应网络参数和快速学习等特点。提出了一种基于遗传算法(GA)优化的BP神经网络的摄像机标定方法,遗传算法可以优化连接权值和阈值等网络参数。对GA-BP神经网络和BP神经网络进行了综合比较。实验结果表明,采用该方法进行GA-BP神经标定是有效可行的。
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