基于PSO和LSSVM回归的摄像机标定

针对摄像机非线性显式标定时很难精确地建立其复杂的数学模型,本文提出了基于粒子群优化算法(PSO)和最小二乘支持向量机(LSSVM)回归的摄像机非线性隐式标定方法.该方法采用最小二乘回归机精确逼近图像坐标与世界坐标之间复杂的非线性成像关系;利用PSO算法搜索LSSVM回归模型的最优参数,提高LSSVM回归的收敛速度和泛化能力.通过运用标准BP神经网络、遗传算法、LSSVM及粒子群优化的LSSVM回归方法对圆阵列图案标定模板进行标定,实验结果表明:基于PSO和LSSVM回归的标定方法具有标定精度高、收敛速度快、泛化能力强等优点.

Camera Calibration Based on PSO and LSSVM Regression

Aiming at the difficulty of establishing accurate mathematical model of camera in explicit non-linearcalibration,a new implicit non-1inear camera calibration method based on Particle Swarm Optimization(PSO)and Least Square Support Vector Machine(LSSVM)regression was proposed.A least square support vector regression machine wasbuilt to exactly approximate to the non-linear imaging relationship between image points and corresponding 3D worldcoordinates.And PSO algorithm was used to search the optimum parameters of the LSSVM regression model to improve the convergence speed and generalization ability.The calibration results of circular template from standard BP neural network,genetic algorithm,LSSVM and particle swarm optimized LSSVM regression,were compared.The comparisonanalysis indicates that the proposed LSSVM regression method based on PSO has advantages such as higher accuracy, faster convergence speed and better generalization ability.