RT Journal Article
T1 Novel Bayesian Inference-Based Approach for the Uncertainty Characterization of Zhang's Camera Calibration Method
A1 Gutiérrez Moizant, Ramón Alberto
A1 López Boada, María Jesús
A1 Ramírez Berasategui, María Beatriz
A1 Al Kaff, Abdulla Hussein Abdulrahman
AB Camera calibration is necessary for many machine vision applications. The calibration methods are based on linear or non-linear optimization techniques that aim to find the best estimate of the camera parameters. One of the most commonly used methods in computer vision for the calibration of intrinsic camera parameters and lens distortion (interior orientation) is Zhang¿s method. Additionally, the uncertainty of the camera parameters is normally estimated by assuming that their variability can be explained by the images of the different poses of a checkerboard. However, the degree of reliability for both the best parameter values and their associated uncertainties has not yet been verified. Inaccurate estimates of intrinsic and extrinsic parameters during camera calibration may introduce additional biases in post-processing. This is why we propose a novel Bayesian inference-based approach that has allowed us to evaluate the degree of certainty of Zhang¿s camera calibration procedure. For this purpose, the a prioriprobability was assumed to be the one estimated by Zhang, and the intrinsic parameters were recalibrated by Bayesian inversion. The uncertainty of the intrinsic parameters was found to differ from the ones estimated with Zhang¿s method. However, the major source of inaccuracy is caused by the procedure for calculating the extrinsic parameters. The procedure used in the novel Bayesian inference-based approach significantly improves the reliability of the predictions of the image points, as it optimizes the extrinsic parameters.
PB MDPI
SN 1424-3210
YR 2023
FD 2023-09-15
LK https://hdl.handle.net/10016/38584
UL https://hdl.handle.net/10016/38584
LA eng
NO This work was supported by the Madrid Government (Comunidad de Madrid Spain) underthe Multiannual Agreement with UC3M ("Fostering Young Doctors Research", APBI-CM-UC3M),and in the context of the VPRICIT (Research and Technological Innovation Regional Programme andby the FEDER/Ministry of Science and Innovation -Agencia Estatal de Investigacion (AEI) of theGovernment of Spain through the projects PID2022-136468OB-I00 and PID2022-142015OB-I00.
DS e-Archivo
RD 30 jun. 2024