Precision Of Camera Calibration Research Articles

High-Precision Calibration Method and Error Analysis of Infrared Binocular Target Ranging Systems

Infrared binocular cameras, leveraging their distinct thermal imaging capabilities, are well-suited for visual measurement and 3D reconstruction in challenging environments. The precision of camera calibration is essential for leveraging the full potential of these infrared cameras. To overcome the limitations of traditional calibration techniques, a novel method for calibrating infrared binocular cameras is introduced. By creating a virtual target plane that closely mimics the geometry of the real target plane, the method refines the feature point coordinates, leading to enhanced precision in infrared camera calibration. The virtual target plane is obtained by inverse projecting the centers of the imaging ellipses, which are estimated at sub-pixel edge, into three-dimensional space, and then optimized using the RANSAC least squares method. Subsequently, the imaging ellipses are inversely projected onto the virtual target plane, where its centers are identified. The corresponding world coordinates of the feature points are then refined through a linear optimization process. These coordinates are reprojected onto the imaging plane, yielding optimized pixel feature points. The calibration procedure is iteratively performed to determine the ultimate set of calibration parameters. The method has been validated through experiments, demonstrating an average reprojection error of less than 0.02 pixels and a significant 24.5% improvement in calibration accuracy over traditional methods. Furthermore, a comprehensive analysis has been conducted to identify the primary sources of calibration error. Ultimately, this achieves an error rate of less than 5% in infrared stereo ranging within a 55-m range.

Two Vanishing Points Error Estimation Based on Line Clustering and Condition Adjustment with Parameters

In close-range digital photogrammetry and computer vision, a major challenge is the automation of 3D reconstruction from 2D-images. And single image calibration is a fundamental task in these areas for research. It is known that camera parameters can be recovered by the vanishing points of three orthogonal directions. However, three reliable and well-distributed vanishing points are not always available. Therefore, how to estimate the error of two vanishing points is very significant for us to analyze the precision of camera calibration. New methods for vanishing point detection and error estimation are presented, which can be illustrated as follows. Firstly, the line clustering, which parallel to object lines and correspond to the vanishing points, are detected based on RANSAC (Random Sample Consensus). Secondly, "condition adjustment with parameters" is utilized to estimate a nonlinear error equation. Thirdly, the error of vanishing point is expressed by error ellipse that is derived by co-factor matrix according to adjustment principle. Finally, experimental results of vanishing points coordinates and their errors are shown and analyzed.

A Technique for Binocular Stereo Vision System Calibration by the Nonlinear Optimization and Calibration Points with Accurate Coordinates

With the increasing need for higher accuracy measurement in computer vision, the precision of camera calibration is a more important factor. The objective of stereo camera calibration is to estimate the intrinsic and extrinsic parameters of each camera. We presented a high-accurate technique to calibrate binocular stereo vision system having been mounted the locations and attitudes, which was realized by combining nonlinear optimization method with accurate calibration points. The calibration points with accurate coordinates, were formed by an infrared LED moved with three-dimensional coordinate measurement machine, which can ensure indeterminacy of measurement is 1/30000. By using bilinear interpolation square-gray weighted centroid location algorithm, the imaging centers of the calibration points can be accurately determined. The accuracy of the calibration is measured in terms of the accuracy in the reconstructing calibration points through triangulation, the mean distance between reconstructing point and given calibration point is 0.039mm. The technique can satisfy the goals of measurement and camera accurate calibration.