BP Neural Network Camera Calibration Based on Particle Swarm Optimization Genetic Algorithm
Jiang Xiang-ku · Jisuanji kexue yu tansuo · 2014
Camera calibration is a key step for extracting three-dimensional information from two-dimensional image,which directly determines the accuracy of 3D reconstruction. In order to solve the problem of multiple parameters,reduce the computational cost, promote the accuracy and speed of camera calibration, this paper firstly applies particle swarm optimization genetic algorithm(PSO-GA) to camera calibration. The initial parameters of the genetic algorithm are optimized by particle swarm optimization. After that, the parameters are optimized by the selection, crossover and mutation operations of genetic algorithm, which can realize the integration of particle swarm optimization and genetic algorithm. The resulting algorithm has stronger global search ability, faster convergence speed, better optimization ability and robustness. At the same time, the camera calibration method based on neural network just can cover very limited calibration space, this paper proposes a new camera calibration method using particle swarmoptimization genetic algorithm to optimize the BP neural network, in order to solve the problem that the traditional camera calibration method is difficult to solve. The experimental data show that the BP neural network calibration based on particle swarm optimization genetic algorithm is a feasible method, which has high calibration precision,fast convergence speed and strong generalization ability.