Camera Calibration without Feature Extraction

This paper presents an original approach to the problem of camera calibration using a calibration pattern. It consists of directly searching for the camera parameters that best project three-dimensional points of a calibration pattern onto intensity edges in an image of this pattern,without explicitly extracting the edges. Based on a characterization of image edges as maxima of the intensity gradient or zero-crossings of the Laplacian, we express the whole calibration process as a one-stage optimization problem. A classical iterative optimization technique is used in order to solve it. Our approach is different from the classical calibration techniques which involve two consecutive stages: extraction of image features and computation of the camera parameters. Thus, our approach is easier to implement and to use, less dependent on the type of calibration pattern that is used, and more robust. First, we describe the details of the approach. Then, we show some experiments in which two implementations of our approach and two classical two-stage approaches are compared. Tests on real and synthetic data allow us to characterize our approach in terms of convergence, sensitivity to the initial conditions, reliability, and accuracy.