Neurocalibration: a Neural Network that can tell

Camera Calibration Parameters

Moumen Ahmed, E. E. Hemayed, A. A. Farag,
IEEE International Conf. on Computer Vision, Sept 1999.

 

 

Abstract

Camera calibration is a primary crucial step in many computer vision tasks. In this paper we present a new neural approach for camera calibration. Unlike some existing neural approaches, our calibrating network can tell the perspective-projection-transformation matrix between the world 3D points and the corresponding 2D image pixels.

Starting from random initial weights, the net can specify the camera model parameters satisfying the orthogonality constraints on the rotational transformation. The neurocalibration technique is shown to solve four different types of calibration problems that are found in computer vision applications. Moreover, it can be extended to the more difficult problem of calibrating cameras with automated active lenses. The validity and performance of our technique are tested with both synthetic data under different noise conditions and with real images. Experiments have shown the accuracy and the efficiency of our neurocalibration technique.