Machine vision is used for applications such as automated inspection, process control and robot guidance, and is directly associated with increasing of manufacturing process flexibility. The presence of noise in image data affects robustness and accuracy of machine vision, which can be an obstacle for industrial applications. Accuracy depends on both feature detection, resulting in pixel values of the measures of interest, and vision systems calibration, which allows transforming pixel measurements into real-world coordinates. This paper analyzes the camera calibration process, and proposes a new method for camera calibration, based on numerical analysis of probability distributions of the calibration parameters and removal of outliers. The method can be used to improve accuracy and robustness of the vision systems calibration process.
Semeniuta, O. (2016). Analysis of Camera Calibration with Respect to Measurement Accuracy. In Procedia CIRP (Vol. 41, pp. 765–770). Elsevier B.V. https://doi.org/10.1016/j.procir.2015.12.108