Flexible camera setup for visual based registration on 2D interaction surface with undefined geometry using neural network

Abstract

Camera setup, calibration and visual based registration of Augmented Reality (AR) based tabletop setups can be a really complicated and time-intensive task. Homography is often used liberally despite its assumption for planar surfaces, where the mapping from the camera to the table can be expressed by a simple projective homography. However, this approach often fails in curved and non-planar surface setups. In this paper, we propose a technique that approximates the values and reduces the tracking error-values by the usage of a neural network function. The final result gives a uniform representation of the camera against combinations of camera parameters that will help in the multi-camera setup. We present the advantages with demonstration applications, where a laser pointer spot and a light from the lamp will be tracked in non planar surface. © 2006 Springer-Verlag Berlin/Heidelberg.