A camera calibration method based on neural network optimized by genetic algorithm

The calibration of camera is to determine the relation between the two dimensional (2D) image coordinates and the corresponding three dimensional (3D) world points, and is the basis of vision inspection system. This paper presents a new neurocalibration approach based on the neural network optimized by Genetic algorithm (GA) for camera calibration. Unlike other existing approaches based on neural network, our calibrating method can give a theoretical optimization solution for the problems in using neural network. We use GA to optimize the structure, the connection weights and the threshold values of the neurons of the neural network. Though the training time of our method is longer than the BP neural network, the experiments results show that the method we proposed is feasible, robust and effective.