Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Geometric Camera Calibration Using Circular Control Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-Accuracy and Robust Localization of Large Control Markers for Geometric Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera Models and Fundamental Concepts Used in Geometric Computer Vision
Foundations and Trends® in Computer Graphics and Vision
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Most calibration methods are based on the camera model which consists of physical parameters of the camera including position, orientation, focal length, and optical center. In this paper, we propose a new approach which is based on the neural network model instead of the physical camera model. The neural network employed in this paper is primarily used as a nonlinear modeling function 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. In order to show the performance of the proposed method, images from two different cameras with three different camera angles were used for calibrating the cameras. The performance of the proposed neural network approach is compared with the well-known Tsai's two stage method in terms of calibration errors. The results show that the proposed approach gives much more stable and acceptable calibration error over Tsai's two stage method regardless of camera camera angle.