A New Efficient Calibration Method for Binocular Camera

In the field of three-dimensional (3-D) metrology based on fringe projection profilometry (FPP), accurate camera calibration is an essential task and a primary requirement. In order to improve the accuracy of camera calibration, the calibration board or calibration target needs to be manufactured with high accuracy, and the marker points in calibration image require to be positioned with high accuracy. This paper presents an improved camera calibration method by simultaneously optimizing the camera parameters and target geometry. Specifically, a set of regularly distributed target markers with rich coded concentric ring pattern is first displayed on a liquid crystal display (LCD) screen. Then, the sub-pixel edges of all coded bands radial straight lines are automatically located at several positions of the LCD screen. Finally, the sub-pixel edge point set is mapped into parameter space to form a line set, and the intersection of the lines is defined as the center pixel coordinates of each target point to complete the camera calibration. The simulation and experimental results verify that the proposed camera calibration method is feasible and easy to operate, which can essentially eliminate the perspective transformation error to improve the accuracy of camera parameters and target geometry.

Camera calibration using synthetic random speckle pattern and digital image correlation

Camera calibration method based on optimal polarization angle

The line structure light three-dimensional reconstruction system is a kind of three-dimensional non-contact measurement system, which has the advantages of high precision, high speed, small damage to objects and strong adaptability. Camera calibration is a major factor that constrains the accuracy of 3D measurement systems. The camera calibration is based on the pinhole imaging model, and through a series of complex calculations, the camera’s internal parameters (focal length, distortion coefficient) and external parameters (rotation matrix and translation vector). The different calibration methods use different calibration targets, which can be divided into 3D calibration targets, 2D calibration targets, and one-dimensional calibration targets according to the characteristics of the calibration targets. This paper mainly discusses: calibration content and significance, calibration methods for different targets and evaluation methods for calibration of different targets. Firstly, the content and significance of calibration are expounded. Then, according to different calibration targets, the calibration algorithm is analyzed. Finally, the calibration algorithm is analyzed and summarized, and the development trends, advantages and disadvantages of different calibration methods are pointed out.

Development of distance measure technology will always make contributions to the industry and society. Therefore, distance measure based on binocular vision technology has become a hot research topic. This kind of distance measure technology has relative high measure accuracy as well as lower hardware cost. However, due to the adoption of inexpensive binocular cameras, the tangential and radial distortions exist. Thus, the distortion will in turn bring measure error. In other words, measure accuracy of system will decline. This paper, we propose a stereo calibration method for binocular cameras. Camera calibration method is employed to obtain the intrinsic and extrinsic as well as distortion parameters of cameras. Stereo calibration is conducted to correlate the left and right image plane. Rectification method is to make left and right image plane satisfy epipolar constraint. Because after rectification, we can reduce computation of matching correspond feature points in left and right image as well as improve system response speed. The average reprojection error can be controlled within 9 pixels.

The calibration of binocular infrared camera is a key step to realize the positioning of infrared camera. The existing methods of binocular camera calibration are not suitable for infrared camera, so it is necessary to study the methods of binocular infrared camera calibration and positioning. In this paper, a calibration and positioning method of binocular infrared camera is proposed, and the infrared positioning of two kinds of objects is completed by using this method. Firstly, a special temperature calibration system is used to collect the images for binocular calibration, then the center point detection algorithm and Zhang’s calibration method are used to process the infrared calibration image to obtain the internal and external parameters of the binocular infrared camera. The calibration error is compared with that of binocular visible camera to prove the feasibility of the binocular infrared camera calibration. Secondly, according to the calibrated parameter matrix of the binocular infrared camera, symmetrical circle calibration object detection algorithm and coordinate transformation are carried out for the distortion correction images to achieve infrared positioning of different objects, and the positioning error is obtained. The results show that the calibration method of binocular infrared camera is feasible, and it has more functions than the traditional calibration method.

In this paper, we propose a method for precise camera calibration which conducts feature point extraction and camera parameter estimation iteratively. Many of conventional researches on camera calibration have focused on how to calculate camera parameters using data obtained from input images, that is, location of feature points. However, these input images suffer from distortions caused by perspective and lens imperfections. In our proposed method, at the beginning of the procedure, projective transformation matrices between image planes and a calibration target and lens distortion parameters are approximately estimated. These parameters are used in order to reduce the influence of distortions in input images. After the removal of distortions, feature points in processed images are localized precisely and used to update/calculate the projective transformation matrices, the lens distortion parameters and intrinsic camera parameters. These procedures are iterated until they converge and this iteration results in precise estimation of the parameters. The effectiveness of the proposed method has been recognized through experiments using synthesized data and real images.

A novel camera calibration method based on circle projection model

Based on analyzing and partially testing the current methods for camera calibration in computer vision, inspired by Zhang’s technique, we introduced a new method for binocular stereovision camera calibration. In this method, a planar object with a series of circles was used as calibration template, and the centers of circles were regarded as control points. The proposed method requires the two cameras with relatively fixed position to simultaneously observe the calibration template at a few (at least two) different orientations by moving the cameras or planar object freely, then extracts the centers’ coordinates of circles or ellipses by image processing technique. On this basis, calculate every camera’ intrinsic and extrinsic parameters, then calculate the position parameters of the two cameras. The main point of this method is that it can match the points in the model plane and their image points easily; compared with Zhang’s calibration methods, it can reduce the errors of extracting control points. Experimental results and contrast tests show proposed method is accurate and very applicative to camera calibration of binocular stereovision system.

A more efficient camera calibration and optimisation method based on orthogonal vanishing points is proposed. After extracting corner points on the calibration images by using the Harris algorithm, some parallel lines are fitted via the least square method. The Levenberg–Marquard (L–M) algorithm is used to find out the optimum vanishing points. The intrinsic parameters are solved on the basis of the orthogonal vanishing point characters. The enhanced Tsai’s method is designed to solve the extrinsic parameters. The minimum residue optimisation function is introduced to calculate the distortion coefficients and optimise all parameters globally. The feasibility and efficiency of the proposed method is illustrated by an experimental study. The method can be used to obtain high precision calibration parameters without the need of sophisticated mechanical devices. It has great potential to calibration cameras in the field of computer vision, 3D measurement, etc.

Camera calibration based on lightweight fan-shaped target detection and fitness-distance-balance chaotic marine predators algorithm

This research aimed to optimize the camera calibration process by identifying the optimal distance and angle for capturing checkered board images, with a specific focus on understanding the factors that influence the reprojection error (ϵRP). The objective was to improve calibration efficiency by exploring the impacts of distance and orientation factors and the feasibility of independently manipulating these factors. The study employed Zhang's camera calibration method, along with the 2k full-factorial analysis method and the Latin Hypercube Sampling (LHS) method, to identify the optimal calibration parameters. Three calibration methods were devised: calibration with distance factors (D, H, V), orientation factors (R, P, Y), and the combined two influential factors from both sets of factors. The calibration study was carried out with three different stereo cameras. The results indicate that D is the most influential factor, while H and V are nearly equally influential for method A; P and R are the two most influential orientation factors for method B. Compared to Zhang's method alone, on average, methods A, B, and C reduce ϵRP by 25%, 24%, and 34%, respectively. However, method C requires about 10% more calibration images than methods A and B combined. For applications where lower value of ϵRP is required, method C is recommended. This study provides valuable insights into the factors affecting ϵRP in calibration processes. The proposed methods can be used to improve the calibration accuracy for stereo cameras for the applications in object detection and ranging. The findings expand our understanding of camera calibration, particularly the influence of distance and orientation factors, making significant contributions to camera calibration procedures.

This paper proposes a calibration method for catadioptric camera systems consisting of a mirror whose reflecting surface is the surface of revolution and a perspective camera as typified by HyperOmni Vision. The proposed method is based on conventional camera calibration and mirror posture estimation. Many methods for camera calibration have been proposed and during the last decade, methods for catadioptric camera calibration have also been proposed. The main problem with catadioptric camera calibration is that the degree of freedom of mirror posture is limited or the accuracy of the estimated parameters is inadequate due to nonlinear optimization. On the other hand, our method can estimate five degrees of freedom of mirror posture and is free from the volatility of nonlinear optimization. The mirror posture has five degrees of freedom, because the mirror surface has a surface of revolution. Our method uses the mirror boundary and can estimate up to four mirror postures. We apply an extrinsic parameter calibration method based on conic fitting for this estimation method. Because an estimate of the mirror posture is not unique, we also propose a selection method for finding the best one. By using the conic-based analytical method we can avoid the initial value problem arising from nonlinear optimization. We conducted experiments on synthesized images and real images to evaluate the performance of our method, and discuss its accuracy.

To binocular vision light pen measuring system, calibrating the camera fast and accurate, can improve the practicality of the system application. In the establishment of ideal camera imaging model, considering the lens radial distortion and tangential distortion factors, studies the practical imaging model parameters. Plane template calibration algorithm is improved. Using points which are near calibration template center and distortion smaller solve the initial model parameters. Application internal mapping Newton's method of subspace confidence domain method solves global optimization model parameters. Designing plane calibration template and using coded identification method, identify the signs point in the calibration template. The calibration efficiency is improved. The experiments show that the calibration method can be achieved binocular stereo vision camera calibration.

High-accuracy binocular camera calibration is a vital basis of precise binocular vision 3D measurement. In this work, a high-precision and robust binocular camera calibration method based on a coding target is proposed. First, a coding target with the simple patterns is designed. Every corner on the coding target has a unique code number, which can make the identification of homonymous corners easier and more valuable, even if the target is partially occluded. The decoding of the coding target is rapid, robust, and accurate at a complex background. Subsequently, the zenith and azimuth angles are introduced in the proposed calibration method to study the effect of the orientation of the coding target on the stability of calibration results and improve the robustness of the calibration results. Finally, to fully utilize the 3D information of the calibration corners on the coding target, we combine the reprojection and 3D geometric constraints to propose a multi-constraint optimization method for refining the parameters of binocular camera and improving the accuracy of binocular camera calibration. The comparison experiments have been done to verify the performance of the proposed calibration method. The standard deviations of the intrinsic and extrinsic parameters are greatly decreased, compared with Zhang's method. The mean reprojection and 3D geometric errors calculated by the proposed method have a large reduction. And the application experiment furtherly validates the effectiveness of the proposed method.