Automatic Perspective Camera Calibration Based on an Incomplete Set of Chessboard Markers

The measurement in metric units from a perspective view of a real world object is an important task in many computer vision applications. The goal of the perspective calibration is to map the reference coordinates of the 3D object into the 2D image coordinates. This is usually done by recovering the perspective transformation parameters by presenting to the camera a view of a known calibration pattern. Many of the available algorithms require to process all the points of the calibration pattern in order to recover the transformation parameters effectively. The approach we present for the computation of the inverse perspective transformation does not require to consider all the grid points. Rather, a grid matching between the 3D points projection and the model is performed exploiting a graph based approach. Extensive experiments have proved the efficacy of the algorithm in different application fields such as camera calibration and automotive headlight beam characterization.