An Efficient Method for Camera Calibration Using MultiLayer Perceptron Type Neural Network

This paper presents a 3D camera calibration method based on a nonlinear modeling function of an artificial neural network. The neural network employed in this paper is primarily used as a nonlinear mapper between 2D image points and points of a certain space in 3D real world. The neural network model implicitly contains all the physical parameters, some of which are very difficult to be estimated in the conventional calibration methods. MutiLayer Perceptron Type Neural Network (MLPNN) is employed to implement the relationship between image coordinates. In order to show the performance of the proposed method, we carry out experiments on the estimation of 2D image coordinates given 3D real world coordinates. The experimental results show that the proposed method improved calibration accuracy over widely used Tsai's two stage method (TSM).