The accuracy of computer vision systems is highly dependent on the correct estimates of the camera intrinsic parameters. This accuracy is important in numerous applications like telepresence and robot navigation. In this work, a novel technique is proposed to model the variation of the camera's intrinsic parameters as a function of the focus and the zoom. The proposed method computes the complete surfaces of the intrinsic parameters from a predefined number of focus/zoom measurements using a moving least-squares (MLS) regression technique. Then, it approximates the generated MLS surfaces by employing adaptive Delaunay meshes. Compared to a previous technique using bivariate polynomial functions, the new method results in a 94% enhancement of the mean estimation error. In addition, the new method leads to the same accuracy of the results as compared to a previous version of the MLS technique while requiring a less amount of computations.
['Michel Sarkis', 'Christian T. Senft', 'Klaus Diepold']
A Novel Technique to Model the Variation of the Intrinsic Parameters of an Automatic Zoom Camera using Adaptive Delaunay Meshes Over Moving Least-Squares Surfaces