Support Vector Machines for Camera Calibration Problem

Abstract

This paper presents a statistical learning-based solution to the camera calibration problem in which the support vector machines (SVM) are used for the estimation of the projection matrix elements. The projection matrix is obtained explicitly by using a dot product kernel in the formulation of the SVM algorithm. The mean field theory is used to approximate an efficient learning procedure for the SVM algorithm. In order to assess the robustness of the proposed approach against noise, the experiments using synthetic data are carried out at different noise levels. The proposed approach is evaluated also with real 3D reconstruction experiments. The experimental results illustrate that the proposed calibration approach is efficient and more robust against noise than other known approaches for camera calibration.