Calibrating an Automatic Zoom Camera With Moving Least Squares

The application of zoom camera lenses in machine vision has gained a lot of attention lately. The main difficulty in their employment lies in the accurate estimation of their intrinsic parameters. In this paper, we propose novel approaches to determine these parameters by estimating continuous models of their variations as the focus and the zoom change. The first method is based on the moving least squares (MLS) multiple regression scheme which determines from a predefined number of samples, the complete model of the intrinsic parameters. MLS fits a polynomial function at each focus and zoom setting by using the measured neighboring points. In order to reduce the computational complexity of MLS, we propose another algorithm in which the MLS generated curves are clustered. Then, each cluster is modeled with a single polynomial function. This decreases the complexity of computations for the applications where delay is critical, e.g., telepresence, to the evaluation of simple polynomials. Compared to previous techniques, the proposed algorithms lead to a noticeable increase in the estimation accuracy of the intrinsic parameters. In addition, they are able to generate accurate models of these parameters with only a few measurement points. Note to Practitioners-Existing calibration methods of automatic zoom cameras solve in general some equations based on common features of a sequence of images. This limits the possibility of calibrating each single image and lets the solution dependent on the accuracy of the features. In this paper, we propose an algorithm based on curve fitting methods. The camera is first calibrated offline at several settings of the zoom and the focus to obtain some measurement points. The unmeasured values are then interpolated by approximating each variable with local polynomial functions. The results obtained show that the method is feasible to be applied in practice. As future work, we will try to address the problem of interpolating the intrinsic parameters, while taking into account their interdependency.