Camera calibration method under poor lighting condition in factories

Abstract

This paper proposes a method to perform accurate camera calibration under poor lighting condition of factories or industrial fields. Preprocessing of camera calibration required for measuring object dimensions has to be able to extract calibration points from patterns of the calibration scale, for example, the calibration from plane pattern scale needs at least seven points of the known dimension marked on the scale. However, industrial fields hardly provide proper lighting condition for camera calibration of the measurement system. The data points for calibration are automatically selected from a probabilistic assumption for size variation of the calibration point when the threshold level changes for image binarization. The system requires user to provide at least four points that are incomplete, these points are used to predict position of exact calibration points and extract accurate calibration parameters in an iterative procedure using nonlinear optimization of the parameters. From real images, we prove the method can be applied to camera calibration of poor quality images obtained under lens distortion and bad illumination.