Auto-calibration may be defined as the process of finding the intrinsic parameters of a camera from real image data. Recent techniques for finding these parameters rely upon solving equations which relate the epipolar geometry of two camera positions with the intrinsic parameters, equations known as Kruppa's equations[4, 2]. These techniques involve a very time consuming numerical process, and yet only produce two of the intrinsic parameters, the focal length and the aspect ratio, to an acceptable degree of accuracy. Further processes which, for example, compute the camera's movement need to assume standard values for the other parameters[4]. In this paper, we present a method of solving Kruppa's equations for the focal length and the aspect ratio which is suitable for a real-time system, together with details of experiments using simulated noisy data which show that its accuracy is comparable with the previous method.
[1]
O. D. Faugeras,et al.
Camera Self-Calibration: Theory and Experiments
,
1992,
ECCV.
[2]
R. Y. Tsai,et al.
An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision
,
1986,
CVPR 1986.
[3]
John E. W. Mayhew,et al.
ANIT - A System for Perceptual Subsumption and Intelligent Vision Systems
,
1994,
BMVC.
[4]
Quang-Tuan Luong.
Matrice Fondamentale et Calibration Visuelle sur l''''Environnement - Vers une plus grande autonomie
,
1992
.
[5]
Richard I. Hartley,et al.
Estimation of Relative Camera Positions for Uncalibrated Cameras
,
1992,
ECCV.