Camera Self-calibration Research Articles

Self-Calibration of Catadioptric Camera with Two Planar Mirrors from Silhouettes

If an object is interreflected between two planar mirrors, we may take an image containing both the object and its multiple reflections, i.e., simultaneously imaging multiple views of an object by a single pinhole camera. This paper emphasizes the problem of recovering both the intrinsic and extrinsic parameters of the camera using multiple silhouettes from one single image. View pairs among views in a single image can be divided into two kinds by the relationship between the two views in the pair: reflected by some mirror (real or virtual) and in a circular motion. Epipoles in the first kind of pairs can be easily determined from intersections of common tangent lines of silhouettes. Based on the projective properties of these epipoles, efficient methods are proposed to recover both the imaged circular points and the included angle between two mirrors. Epipoles in the second kind of pairs can be recovered simultaneously with the projection of intersection line between two mirrors by solving a simple 1D optimization problem using the consistency constraint of epipolar tangent lines. Fundamental matrices among views in a single image are all recovered. Using the estimated intrinsic and extrinsic parameters of the camera, a euclidean reconstruction can be obtained. Experiments validate the proposed approach.

Efficient camera self-calibration method based on the absolute dual quadric

Visual measurement technology has been widely used in fields such as industrial production or measurement and monitoring. Camera calibration is a very important link of visual measurement, because it directly determines the accuracy and precision of visual measurement. It often does not need extremely high-precision measurement but simple, rapid, effective measurement in many engineering surveys. Therefore, the camera self-calibration technique is really needed. The advantages of camera self-calibration are that it does not need any calibration target or complex mechanical structure that is used to control the camera's motion. In this paper, we propose an efficient camera self-calibration method based on the abstract quadric that is simple for calculation and has good robustness.

A Camera Self-calibration Method using a Regular Prismoid

A Camera Self-calibration Method using a Regular Prismoid

Study on Camera Self-Calibration for Intrinsic Focus Parameter Based on One Image

Study on Camera Self-Calibration for Intrinsic Focus Parameter Based on One Image

Self-Calibration of Pan-Tilt Camera Based on Accurate Model

To realize accurate self-calibration of pan-tilt camera with nonideal structure,an accurate pan-tilt camera model is built,and a self-calibration method for intrinsic camera parameters and pan-tilt structural parameters is proposed based on self-constraint characteristics in rotation.Firstly,according to the structural feature of the system,an accurate pan-tilt camera model,different from the ideal model,is built to describe the relative orientation and position offset among pan axes,tilt axes and the camera.Secondly,fixed entities in rotation are calculated by taking advantage of the self-constraint characteristics in rotation,and then combining with the polarizing constraint,the constraint equations of the image for an absolute conic are established and computed to obtain the intrinsic camera parameters by Cholesky's factorization.Finally,based on the intrinsic matrix of the camera,the pan-tilt structural parameters are solved by using the projection characteristics of the rotation axes and cross sections.Experimental results indicate that,for 0.5 pixel noise level,the calibration errors in focal length are less than 0.73%and in principle points are less than 0.52%,and mean error in reprojection of real images is 2.38,all of which are better than those by self-calibration method based on ideal model.The whole calibration process only utilizes the geometric constraint in active rotation of pan-tilt camera,without the information of outside scene or calibration object.

A Survey on Extrinsic Self-calibration Algorithms for Cameras without Overlapping Field of Views

Extrinsic self-calibration of cameras,i.e.,to automatically determine the extrinsic parameters of the cameras(position and orientation),is regarded as an important preparatory requirement.However,if the cameras do not share overlapping field of views(FoVs),they lack the common information,which is normally obtained from the shared FoVs and used to design the self-calibration algorithms.Hence,it is more difficult and challenging to design the extrinsic self-calibration algorithm in non-overlapping camera networks.In this paper,the existing extrinsic camera self-calibration algorithms for non-overlapping camera networks are reviewed,classified and summarized in detail.

Range camera self-calibration with independent object space scale observations

The geometric calibration of range cameras can be performed by means of an integrated self-calibrating bundle adjustment of measurements to signalised targets. The calibration solution quality, as indicated by the correlation between the camera position and the rangefinder offset, is degraded by the need to use a planar target field. In this contribution the inclusion of inter-camera base distances and inter-target distances in the bundle adjustment are investigated. The results indicate that although neither measure can reduce the correlation, solution quality, as measured by independent image orientation, can be improved when long object distances are used in the self-calibration adjustment.

Range camera self-calibration with scattering compensation

Time-of-flight range camera data are prone to the scattering bias caused by multiple internal reflections of light received from a highly reflective object in the camera’s foreground that induce a phase shift in the light received from background targets. The corresponding range bias can have serious implications on the quality of data of captured scenes as well as the geometric self-calibration of range cameras. In order to minimise the impact of the scattering range biases, the calibration must be performed over a planar target field rather than a more desirable 3D target field. This significantly impacts the quality of the rangefinder offset parameter estimation due to its high correlation with the camera perspective centre position. In this contribution a new model to compensate for scattering-induced range errors is proposed that allows range camera self-calibration to be conducted over a 3D target field. Developed from experimental observations of scattering behaviour under specific scene conditions, it comprises four new additional parameters that are estimated in the self-calibrating bundle adjustment. The results of experiments conducted on five range camera datasets demonstrate the model’s efficacy in compensating for the scattering error without compromising model fidelity. It is further demonstrated that it actually reduces the rangefinder offset-perspective centre correlation and its use with a 3D target field is the preferred method for calibrating narrow field-of-view range cameras.

A Stratified Optimization Method for Camera Self-Calibration

We present an efficient stratified optimization approach for self-calibration of a camera in the case that its focal length and the principal point location are unknown. Generally we can assume that the two views are of the same focal length, and the pixels are nearly perfectly rectangular, also it is possible to know the aspect ratio rather accurately. In our approach, we use singular value decomposition to solve a modified Kruppa Equation to derive the focal length with the supposition that the principal point is at the center of the image, and perform an exhaustive search for the principal point near the center of the image to minimize a cost function. We can get a much accurate result with the optimized principal point location.

스테레오 영상을 이용한 자기보정 및 3차원 형상 구현

A 3D reconstruction technique from stereo images that requires minimal intervention from the user has been developed. The reconstruction problem consists of three steps of estimating specific geometry groups. The first step is estimating the epipolar geometry that exists between the stereo image pairs which includes feature matching in both images. The second is estimating the affine geometry, a process to find a special plane in the projective space by means of vanishing points. The third step, which includes camera self-calibration, is obtaining a metric geometry from which a 3D model of the scene could be obtained. The major advantage of this method is that the stereo images do not need to be calibrated for reconstruction. The results of camera calibration and reconstruction have shown the possibility of obtaining a 3D model directly from features in the images.

A RIGOROUS AND FLEXIBLE CALIBRATION METHOD FOR DIGITAL AIRBORNE CAMERA SYSTEMS

Abstract. This paper presents a novel family of rigorous and flexible mathematical self-calibration additional parameters (APs) for airborne camera calibration. It is pointed out, that photogrammetric self-calibration can – to a very large extent – be considered as a function approximation problem in mathematics. Based on the mathematical approximation theory, we suggest that Fourier series (trigonometric polynomials) be the optimal mathematical basis functions for camera self-calibration. The whole family of so-called Fourier Self-calibration APs is developed, whose solid theoretical foundations are Laplace Equation and Fourier Theorem. The Fourier APs are mathematically rigorous, orthogonal, flexible, generic and efficient for calibrating the image distortion of frame-format airborne cameras. We show the theoretical and practical advantages of Fourier APs over the popular polynomial APs and physical APs. The good performance of Fourier APs is illustrated in the many practical tests on camera system calibration, including the DMC, DMCII, UltracamX, UltracamXp, DigiCAM cameras.

Self-calibration of wireless cameras with restricted degrees of freedom

This paper presents an approach for the automatic calibration of low-cost cameras which are assumed to be restricted in their freedom of movement to either pan or tilt movements. Camera parameters, including focal length, principal point, lens distortion parameter and the angle and axis of rotation, can be recovered from a minimum set of two images of the camera, provided that the axis of rotation between the two images goes through the camera’s optical center and is parallel to either the vertical (panning) or horizontal (tilting) axis of the image. Previous methods for auto-calibration of cameras based on pure rotations fail to work in these two degenerate cases. In addition, our approach includes a modified RANdom SAmple Consensus (RANSAC) algorithm, as well as improved integration of the radial distortion coefficient in the computation of inter-image homographies. We show that these modifications are able to increase the overall efficiency, reliability and accuracy of the homography computation and calibration procedure using both synthetic and real image sequences.

Self-calibration of hybrid central catadioptric and perspective cameras

Hybrid central catadioptric and perspective cameras are desired in practice, because the hybrid camera system can capture large field of view as well as high-resolution images. However, the calibration of the system is challenging due to heavy distortions in catadioptric cameras. In addition, previous calibration methods are only suitable for the camera system consisting of perspective cameras and catadioptric cameras with only parabolic mirrors, in which priors about the intrinsic parameters of perspective cameras are required. In this work, we provide a new approach to handle the problems. We show that if the hybrid camera system consists of at least two central catadioptric and one perspective cameras, both the intrinsic and extrinsic parameters of the system can be calibrated linearly without priors about intrinsic parameters of the perspective cameras, and the supported central catadioptric cameras of our method can be more generic. In this work, an approximated polynomial model is derived and used for rectification of catadioptric image. Firstly, with the epipolar geometry between the perspective and rectified catadioptric images, the distortion parameters of the polynomial model can be estimated linearly. Then a new method is proposed to estimate the intrinsic parameters of a central catadioptric camera with the parameters in the polynomial model, and hence the catadioptric cameras can be calibrated. Finally, a linear self-calibration method for the hybrid system is given with the calibrated catadioptric cameras. The main advantage of our method is that it cannot only calibrate both the intrinsic and extrinsic parameters of the hybrid camera system, but also simplify a traditional nonlinear self-calibration of perspective cameras to a linear process. Experiments show that our proposed method is robust and reliable.

A New Method of Camera Self-calibration Based on Relative Lengths

A New Method of Camera Self-calibration Based on Relative Lengths

An Approach for Camera Self-Calibration using Vanishing-Line

An Approach for Camera Self-Calibration using Vanishing-Line

Camera Self-calibration Using Multiple Geometric Constraints in a Single Image

Camera self-calibration is a key step to acquisition 3D space information from 2D image,and it is always one of the important issues in photogrammetry.However,present methods for camera self-calibration need two or more images and/or their corresponding points.With the development of digital devices for image taken and(wireless) network,a method not depending on digital device,images taken process,or multiple images,is badly needed.Consequently this paper presented a novel method that makes full use of various geometric constraints to realize reliable camera calibration for a single image.Firstly,this paper summarized various geometric constraints and invariants for the existing camera self-calibration method.Secondly,in order to build the relationships among geometric constraints for calibration,we coded for different planes and geometric features in an image.Because variance represents the error distribution,it can be considered as the determinant.In this paper,we obtained the variance of different combination of geometric features for camera calibration by means of fitting each groups of geometric features for thirty times,and then depended on the variance above to determine the weight of each camera's internal parameters.Finally,based on each camera's internal parameters,here we only focus on foci length,and their corresponding weights,the ultimate results are computed.Two images which depict inside and outside scene respectively were chosen to test the usability of our methods.In order to avoid the influence of image distortion,we corrected it using amethod we proposed in another paper before tests.The test results show that: 1) the weighted method gave a more stable result,relative to the result of each group geometric constraints,that is one group's relative error is two high and in other may be lower;2) the weighted method obtained a higher accuracy result than the mean of all groups.The results of verification testing for the two images of the indicated that our weighted method can comprehensive employs variety of geometric constraints in single image,in the other side,it also takes their corresponding variance into account.It makes full use of the variety,usability and stability of geometric constraints.It can be employed to images depict indoor and outdoor which contains more geometric constraints.

基于同心圆光栅和契形光栅的摄像机自标定方法

基于同心圆光栅和契形光栅的摄像机自标定方法

Research on Camera Self-Calibration of High-Precision in Binocular Vision

This paper presents a self-calibration method of binocular vision; Frist,to match the point coordinates to be normalized to calculate fundamental matrix. Then solving the Kruppa equation by decomposing the fundamental matrix, getting the camera parameters. By using the principle of the same name point lights Intersect at one point to get external camera parameters that dual camera relative position and attitude. Finally, by introducing the principles of camera non-linear distortion of regional adjustment to improve the accuracy of calibration. By comparison between simulation and experimental and real analysis, the result shows that the method is a relatively accurate and practical self-calibration binocular method.

A Method of Planar-Template- Based Camera Self-Calibration for Single-View

A Method of Planar-Template- Based Camera Self-Calibration for Single-View

Camera Self-Calibration with Planar Pattern Using Genetic Algorithm

In the paper, a stereo camera self-calibration method with Genetic Algorithm (GA) applied to the navigation of autonomous land vehicle (ALV) in a natural environment is proposed. The proposed method does not require specific object as a calibration pattern, e.g. checkerboard; conversely, it exploits common feature, for example: planes among natural scenes. In the evaluating process of GA, the coplanar condition of 3D points is employed as a fitness function to inspect the camera parameters. In addition, real valued GA is used because it does not only decrease the complexity of encoding and decoding process, but also increase the precision of solution. Comparing to conventional optimization methods, the camera self-calibration method based on GA can avoid being trapped in local minimum and does not need initial value or gradient information. Several experiments of the camera calibration with the stereo vision show that the proposed method can find approximate optimum solution.