A Camera Calibration Method Using Quadratic Curve Fitting

2012 ◽  
Vol 591-593 ◽  
pp. 1281-1284
Author(s):  
Tian Xia ◽  
Chao Jin ◽  
Xiao Yang Jiang ◽  
Yi Zhong Li
Keyword(s):  
Camera Calibration ◽  
Curve Fitting ◽  
Calibration Method ◽  
Model Parameters ◽  
Feature Points ◽  
Camera Model ◽  
Quadratic Curve ◽  
Camera Image ◽  
Concentric Circles ◽  
The Relationship

The camera calibration is an essential part in the machine vision,the camera calibration is to establish the relationship between the camera image pixellocation and scene position,the approach is based on the camera model,solving the model parameters of the camera image coordinates and world coordinates from the known feature points. Using the camera calibration method based on quadratic curve,this article selects a template consists of two concentric circles and two concentric ovals.We shot 4 images in different directions,and use different combinations of three of the four images to calibrate camera.Thus we can calculatethe various parameters of the camera.

scholarly journals Charuco Board-Based Omnidirectional Camera Calibration Method

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 421 ◽  
Author(s):  
Gwon An ◽  
Siyeong Lee ◽  
Min-Woo Seo ◽  
Kugjin Yun ◽  
Won-Sik Cheong ◽  
...  
Keyword(s):  
Camera Calibration ◽  
Mean Absolute Error ◽  
Three Dimensional ◽  
Absolute Error ◽  
Calibration Method ◽  
Feature Points ◽  
Omnidirectional Camera ◽  
Reprojection Error ◽  
Conventional Methods ◽  
Better Than

In this paper, we propose a Charuco board-based omnidirectional camera calibration method to solve the problem of conventional methods requiring overly complicated calibration procedures. Specifically, the proposed method can easily and precisely provide two-dimensional and three-dimensional coordinates of patterned feature points by arranging the omnidirectional camera in the Charuco board-based cube structure. Then, using the coordinate information of the feature points, an intrinsic calibration of each camera constituting the omnidirectional camera can be performed by estimating the perspective projection matrix. Furthermore, without an additional calibration structure, an extrinsic calibration of each camera can be performed, even though only part of the calibration structure is included in the captured image. Compared to conventional methods, the proposed method exhibits increased reliability, because it does not require additional adjustments to the mirror angle or the positions of several pattern boards. Moreover, the proposed method calibrates independently, regardless of the number of cameras comprising the omnidirectional camera or the camera rig structure. In the experimental results, for the intrinsic parameters, the proposed method yielded an average reprojection error of 0.37 pixels, which was better than that of conventional methods. For the extrinsic parameters, the proposed method had a mean absolute error of 0.90° for rotation displacement and a mean absolute error of 1.32 mm for translation displacement.

Based on the Experimental Platform of Litchi Picking Vision Calibration

2013 ◽  
Vol 462-463 ◽  
pp. 978-983
Author(s):  
Jiang Zhou Zhang ◽  
Jian Feng Zhang ◽  
Ji Zhong Deng
Keyword(s):  
Binocular Vision ◽  
Calibration Method ◽  
Feature Points ◽  
Camera Model ◽  
Reference Coordinate System ◽  
Experimental Platform ◽  
Hardware System ◽  
Mechanical Hand ◽  
Planar Pattern ◽  
Selection Of

This paper constructs the picking mechanical hand binocular vision hardware system. Using an internal and external parameters calibration method of separation. In the calibration process, with calibration planar pattern translatory manner, appropriate for extracting feature points in the calibration template, through experiment and calculation to obtain the accurate parameters of camera model; external parameters calibration is a reasonable selection of reference coordinate system, through experimental analysis to obtain the two cameras outside parameter model.

scholarly journals DoF-Dependent and Equal-Partition Based Lens Distortion Modeling and Calibration Method for Close-Range Photogrammetry

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5934
Author(s):  
Xiao Li ◽  
Wei Li ◽  
Xin’an Yuan ◽  
Xiaokang Yin ◽  
Xin Ma
Keyword(s):  
Optical Axis ◽  
Focal Length ◽  
Calibration Method ◽  
Depth Of Field ◽  
Model Parameters ◽  
Lens Distortion ◽  
Camera Model ◽  
Distortion Model ◽  
Manual Adjustment ◽  
Close Range

Lens distortion is closely related to the spatial position of depth of field (DoF), especially in close-range photography. The accurate characterization and precise calibration of DoF-dependent distortion are very important to improve the accuracy of close-range vision measurements. In this paper, to meet the need of short-distance and small-focal-length photography, a DoF-dependent and equal-partition based lens distortion modeling and calibration method is proposed. Firstly, considering the direction along the optical axis, a DoF-dependent yet focusing-state-independent distortion model is proposed. By this method, manual adjustment of the focus and zoom rings is avoided, thus eliminating human errors. Secondly, considering the direction perpendicular to the optical axis, to solve the problem of insufficient distortion representations caused by using only one set of coefficients, a 2D-to-3D equal-increment partitioning method for lens distortion is proposed. Accurate characterization of DoF-dependent distortion is thus realized by fusing the distortion partitioning method and the DoF distortion model. Lastly, a calibration control field is designed. After extracting line segments within a partition, the de-coupling calibration of distortion parameters and other camera model parameters is realized. Experiment results shows that the maximum/average projection and angular reconstruction errors of equal-increment partition based DoF distortion model are 0.11 pixels/0.05 pixels and 0.013°/0.011°, respectively. This demonstrates the validity of the lens distortion model and calibration method proposed in this paper.

Monocular Camera Calibration Method for Robot System

2015 ◽  
Vol 741 ◽  
pp. 697-700 ◽  
Author(s):  
Li Lun Huang ◽  
Wen Guo Li ◽  
Qi Le Yang ◽  
Ying Chun Chen
Keyword(s):  
Edge Detection ◽  
Camera Calibration ◽  
Calibration Method ◽  
Feature Points ◽  
Circular Array ◽  
Robot System ◽  
Canny Edge Detection ◽  
Basic Principles ◽  
Center Point ◽  
Canny Edge

The basic principles of camera calibration are first analyzed, and the method of camera calibrate based on 2D plane circular array is presented. The first process is the use of the canny edge detection operator, and get the edge coordinates of ellipse. Then the ellipse is fitted to obtain the center point of the ellipse, and the centre point coordinates of ellipse is used to regard the feature points to implement camera caliblation. Finally, Zhang Zhengyou's method is used to obtain internal and external parameters of camera. This calibration method can be used to calbration of robot system.

scholarly journals Monocular Vision Ranging and Camera Focal Length Calibration

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lixia Xue ◽  
Meian Li ◽  
Liang Fan ◽  
Aixia Sun ◽  
Tian Gao
Keyword(s):  
Camera Calibration ◽  
Polynomial Regression ◽  
Focal Length ◽  
Three Dimensional ◽  
Calibration Method ◽  
Monocular Vision ◽  
Field Of Vision ◽  
Average Accuracy ◽  
Ground Object ◽  
The Relationship

The camera calibration in monocular vision represents the relationship between the pixels’ units which is obtained from a camera and the object in the real world. As an essential procedure, camera calibration calculates the three-dimensional geometric information from the captured two-dimensional images. Therefore, a modified camera calibration method based on polynomial regression is proposed to simplify. In this method, a parameter vector is obtained by pixel coordinates of obstacles and corresponding distance values using polynomial regression. The set of parameter’s vectors can measure the distance between the camera and the ground object in the field of vision under the camera’s posture and position. The experimental results show that the lowest accuracy of this focal length calibration method for measurement is 97.09%, and the average accuracy was 99.02%.

A Novel Camera Calibration Method Based on Circle Array Target

2012 ◽  
Vol 472-475 ◽  
pp. 968-973
Author(s):  
Hong Ru Wang ◽  
Wen Ding
Keyword(s):  
Camera Calibration ◽  
Visual Inspection ◽  
Assembly Line ◽  
Calibration Method ◽  
Lens Distortion ◽  
Feature Points ◽  
Improved Genetic Algorithm ◽  
Improve Accuracy ◽  
Two Stages ◽  
The Given

To improve accuracy of computer visual inspection in keyboard automatic assembly line, a new two-stage camera calibration method was presented. 2D circle array was used as calibration plate, and centers of the circles were taken as feature points. And feature point coordinates were extracted without human interference. The proposed camera calibration method was divided into two stages. First, lens distortion was neglected, internal and external parameters of the camera were obtained by modified camera calibration toolbox for MATLAB. Then, lens distortion was taken into account, and improved genetic algorithm (GA) was adopted to optimize camera parameters gotten in the first stage. Experiment results indicate the proposed method is feasible, and can meet with requirements of the given application.

Camera Calibration Method Based on Vanishing Point

2014 ◽  
Vol 513-517 ◽  
pp. 3719-3722
Author(s):  
Wen Guo Li ◽  
Shao Jun Duan
Keyword(s):  
Camera Calibration ◽  
Calibration Method ◽  
Experimental Results ◽  
Vanishing Point ◽  
Feature Points ◽  
Vanishing Points ◽  
Parallel Lines ◽  
Internal Parameters ◽  
The Matrix ◽  
Calibration Algorithm

We present a camera calibration method based on vanishing point, that is, the vanishing points of two groups of parallel lines on the target plane are used to achieve camera calibration. A series of known positions points on target plane are used as the feature points, and the target images are recorded, the image coordinates of feature points are used to calculate the coordinates of vanishing point, then the matrix between feature points and camera is used to obtain internal parameters of camera. Experimental results show that the proposed calibration algorithm is correct, simple and convenient.

A Novel Method of Stereo Camera Calibration Using BP Neural Network

2010 ◽  
Vol 29-32 ◽  
pp. 2692-2697
Author(s):  
Jiu Long Xiong ◽  
Jun Ying Xia ◽  
Xian Quan Xu ◽  
Zhen Tian
Keyword(s):  
Neural Network ◽  
Linear Relationship ◽  
Camera Calibration ◽  
Bp Neural Network ◽  
Calibration Method ◽  
Two Stage ◽  
Non Linear ◽  
Novel Method ◽  
Calibration Accuracy ◽  
The Relationship

Camera calibration establishes the relationship between 2D coordinates in the image and 3D coordinates in the 3D world. BP neural network can model non-linear relationship, and therefore was used for calibrating camera by avoiding the non-linear factors of the camera in this paper. The calibration results are compared with the results of Tsai’s two stage method. The comparison show that calibration method based BP neural network improved the calibration accuracy.

scholarly journals A Novel Calibration Board and Experiments for 3D LiDAR and Camera Calibration

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1130 ◽  
Author(s):  
Huaiyu Cai ◽  
Weisong Pang ◽  
Xiaodong Chen ◽  
Yi Wang ◽  
Haolin Liang
Keyword(s):  
Coordinate System ◽  
Camera Calibration ◽  
Interpolation Method ◽  
Calibration Method ◽  
Extraction Methods ◽  
Depth Information ◽  
Feature Points ◽  
Main Plane ◽  
Reprojection Error ◽  
The Difference

Aiming at the problems of feature point calibration method of 3D light detection and ranging (LiDAR) and camera calibration that are calibration boards in various forms, incomplete information extraction methods and large calibration errors, a novel calibration board with local gradient depth information and main plane square corner information (BWDC) was designed. In addition, the "three-step fitting interpolation method" was proposed to select feature points and obtain the corresponding coordinates of feature points in the LiDAR coordinate system and camera pixel coordinate system based on BWDC. Finally, calibration experiments were carried out, and the calibration results were verified by methods such as incremental verification and reprojection error comparison. The calibration results show that using BWDC and the "three-step fitting interpolation method" can solve quite accurate coordinate transformation matrix and intrinsic and external parameters of sensors, which dynamically change within 0.2% in the repeatable experiments. The difference between the experimental value and the actual value in the incremental verification experiment is about 0.5%. The average reprojection error is 1.8312 pixels, and the value changes at different distances do not exceed 0.1 pixels, which also show that the calibration method is accurate and stable.

Implementation of Camera Calibration Method Based on OpenCV

2014 ◽  
Vol 602-605 ◽  
pp. 3796-3799 ◽  
Author(s):  
Ling Zhang ◽  
Mu Yi Yin ◽  
Wei Li ◽  
Hai Lin Liu
Keyword(s):  
Computer Vision ◽  
3D Reconstruction ◽  
Open Source ◽  
Camera Calibration ◽  
Calibration Method ◽  
Camera Model ◽  
Vision Systems ◽  
Radial Distortion ◽  
The Ideal

Aimed at the applications of technology of camera calibration to 3D reconstruction, the ideal camera model is discussed, especially on the influences and solving methods of lens radial distortion andtangential distortion, and an arithmetic of camera calibration based on OpenCV (open source computer vision library) in Visual C++ environment is given. This arithmetic makes use of the functions of the library effectively, so it has high calibration precision and good robust. It can meet the needs of august reality and othercomputer vision systems.

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