激光与光电子学进展, 2018, 55 (12): 121502, 网络出版: 2019-08-01
基于量子粒子群优化算法的摄像机标定优化方法 下载: 831次
Optimization Method of Camera Calibration Based on Quantum-Behaved Particle Swarm Optimization Algorithm
机器视觉 摄像机标定 量子粒子群优化(QPSO)算法 内外参数 machine vision camera calibration quantum-behaved particle swarm optimization (QPSO) intrinsic and extrinsic parameters
摘要
提出一种基于量子粒子群算法的摄像机标定优化方法。通过MATLAB软件的标定程序快速获得摄像机的内外参数;利用量子粒子群优化算法,建立了目标函数,进一步优化摄像机参数。实验结果表明,所提优化算法收敛快,精度高,能在一定程度上提高摄像机的标定精度。
Abstract
A method based on quantum-behaved particle swarm algorithm is proposed to optimize camera parameters. The intrinsic and extrinsic parameters of the camera are quickly obtained by the self-calibration program in MATLAB software, and the camera parameters are optimized by using the quantum-behaved particle swarm optimization algorithm. The experimental results show that the optimization algorithm can converge quickly and with high precision, and it can improve the camera calibration accuracy to some extent.
王道累, 胡松. 基于量子粒子群优化算法的摄像机标定优化方法[J]. 激光与光电子学进展, 2018, 55(12): 121502. Daolei Wang, Song Hu. Optimization Method of Camera Calibration Based on Quantum-Behaved Particle Swarm Optimization Algorithm[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121502.