Camera calibration with precise extraction of feature points using projective transformation

A high-precision camera calibration method for binocular stereo vision system based on a multi-view template and alternative bundle adjustment is presented in this paper. The proposed method could be achieved by taking several photos on a specially designed calibration template that has diverse encoded points in different orientations. In this paper, the method utilized the existing algorithm used for monocular camera calibration to obtain the initialization, which involves a camera model, including radial lens distortion and tangential distortion. We created a reference coordinate system based on the left camera coordinate to optimize the intrinsic parameters of left camera through alternative bundle adjustment to obtain optimal values. Then, optimal intrinsic parameters of the right camera can be obtained through alternative bundle adjustment when we create a reference coordinate system based on the right camera coordinate. We also used all intrinsic parameters that were acquired to optimize extrinsic parameters. Thus, the optimal lens distortion parameters and intrinsic and extrinsic parameters were obtained. Synthetic and real data were used to test the method. The simulation results demonstrate that the maximum mean absolute relative calibration errors are about 3.5e-6 and 1.2e-6 for the focal length and the principal point, respectively, under zero-mean Gaussian noise with 0.05 pixels standard deviation. The real result shows that the reprojection error of our model is about 0.045 pixels with the relative standard deviation of 1.0e-6 over the intrinsic parameters. The proposed method is convenient, cost-efficient, highly precise, and simple to carry out.

It is proposed a method for camera calibration that could be used in stereo systems as well as in stereo head navigation in this paper. A pinhole camera model and two-dimensional planar target are considered. An Iterated Extended Kalman Filter (IEKF) is used to estimate camera parameters. The met hod takes the observed feature points of images as the filter input and the estimated value of the intrinsic and extrinsic camera parameters as the filter output. Both computer simulation and real data experiments have been used to test the proposed method, and good results have been obtained. The RMS error of absolute distance between reprojection feature points is about 0.09 pixels in real experiments. The experimental results show IEKF is also a feasible optimization algorithm for on-line camera calibration. Key words: Camera Calibration, Iterated Extended Kalman Filter, planar target 1. INTRODUCTION Camera calibration is a crucial phase in most vision systems and a first step in 3D reconstruction. It has been broadly applied in machine vision, virtual reality, and three-dimensional reconstruction and so on. Generally, in order to obtain higher calibration precision, in trinsic and extrinsic camera para meters are estimated through nonlinear optimization methods with information acquired from images. Starting from the simplest method we could mention the Least Square Error (LSE)

The key of camera calibration is to calculated the initial value of extrinsic and intrinsic camera parameters. Since the calculation of initial value is not a purely linear problem, 3D measurement products are often using a fixed focal length camera calibration method, but the measuring exists the problem of inconvenience of usage and a limited measurement-range. We have derived a novel camera calibration method which focal length of camera can be variable. By means of using the orthogonal rotation matrix and constraints among camera parameters in the single-view vision, a new algorithm to linearly and exactly calibrate the camera from single-view at least six known feature points is developed. The theoretical analysis and the experiments have demonstrated that the suggested algorithm is fast, exact, efficient and rather robust against noise, and it can be applied to the area of 3D measurement of variable focal length.

In this paper we present an easy method for multiple camera calibration with common field of view only from vertical lines. The locations of the vertical lines are known in advance. Compared to other calibration objects, the vertical lines have some good properties, since they can be easily built and can be visible by cameras in any direction simultaneously. Given 5 fixed vertical lines, an image containing them taken by a camera may provide 2 constraints in the intrinsic parameters of the camera, and extrinsic parameters can then be recovered. The calibration procedure consists of three main steps: Firstly, the image is rectified by a homography, which makes the projections of vertical lines parallel to u-axis in the rectified image. Secondly, for any vertical scan line in the rectified image, if we consider the scan line is taken by a virtual 1D camera, then we can calibrate the 1D camera. Finally, the intrinsic parameters of the original camera can be determined from the intrinsic parameters of the virtual 1D camera. By evaluating on both simulated and real data we demonstrate that our method is efficient and robust.

Camera calibration using one-dimensional information and its applications in both controlled and uncontrolled environments

A new method of camera calibration is proposed for high-precise videometrics in large field. The camera to be calibrated and the control points used in the new method are both close to the ground. In the condition that the control points and the optical center of the camera are approximately coplanar, the principle point position, focal length, lens distortion coefficients and the camera's position and attitude parameters are calibrated precisely by the method. Two calibration images are taken by the camera to be calibrated in measurement state and vertical rotation state respectively. If the vertical tangent lens distortion can be neglected or the movement field of the targets to be measured are close to the ground, only the measurement state calibration image is needed to calibrate the camera's parameters except the vertical tangent lens distortion coefficients. By the new method, to cali- brate the camera's intrinsic parameters in laboratory in advance is not needed. The new method breaks the localization for the traditional camera calibration methods in large field videometrics which require the control points must be distributed in space rationally.

In this paper, we propose a dynamic camera calibration and object extraction method for sport videos captured with a moving pan-tilt-zoom camera. Such technology realizes an immersive free-viewpoint experience whereby audiences can see real sport scenes from any viewpoint. Camera calibration and object extraction are two of the most important processes for rendering free-viewpoint video, since a 3-dimensional model of each object needs to be reconstructed in every frame based on objects' textures and camera parameters. Most conventional rendering methods only use static cameras whose camera parameters and background models change little. However, since the cameras have to be set apart widely enough to capture the entire scene, the resolution of each object becomes low and is not sufficient for rendering high-quality free-viewpoint video. In order to obtain the texture of an object in high resolution from a moving pan-tilt-zoom camera, our proposed method estimates camera parameters by identifying reliable corresponding feature points between video frames, and also extracts the precise textures of objects using estimated camera parameters. Experimental results revealed that the proposed method successfully estimated precise camera parameters compared to the conventional method. Furthermore, by applying our proposed approach, the free-viewpoint video was rendered without visual defects.

In the classic two-step technique based camera calibration methods, pinhole parameters and lens distortion parameters are estimated together. The calibration results are affected by the coupling of these parameters. Image pixel coordinate expressed lens distortion models are derived, which are modifications to existing image physical coordinate expressed lens distortion models and reduce the coupling between linear parameters and distortion parameters. Camera calibration method is proposed based on the new distortion models and used to calibrate binocular vision cameras with a three-dimensional target. The experiment results show that compared with traditional camera calibration method, the proposed method is feasible and can efficiently improve calibration accuracy.

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.

Based on local invariant feature points and cross ratio principle, this paper presents a feature-point-based image watermarking scheme. It is robust to geometric attacks and some signal processes. It extracts local invariant feature points from the image using the improved scale invariant feature transform algorithm. Utilizing these points as vertexes it constructs some quadrilaterals to be as local feature regions. Watermark is inserted these local feature regions repeatedly. In order to get stable local regions it adjusts the number and distribution of extracted feature points. In every chosen local feature region it decides locations to embed watermark bits based on the cross ratio of four collinear points, the cross ratio is invariant to projective transformation. Watermark bits are embedded by quantization modulation, in which the quantization step value is computed with the given PSNR. Experimental results show that the proposed method can strongly fight more geometrical attacks and the compound attacks of geometrical ones.

In general, video see-through based augmented reality (AR) cannot change the magnification of camera zooming parameter due to the difficulty of dealing with changes in intrinsic camera parameters. To realize the usage of camera zooming in AR, we propose a novel simultaneous intrinsic and extrinsic camera parameter estimation method based on an energy minimization framework. Our method is composed of the online and offline stages. An intrinsic camera parameter change depending on the zoom values is calibrated in the offline stage. Intrinsic and extrinsic camera parameters are then estimated based on the energy minimization framework in the online stage. In our method, two energy terms are added to the conventional marker-based camera parameter estimation method. One is reprojection errors based on the epipolar constraint. The other is the constraint of continuity of zoom values. By using a novel energy function, our method can estimate accurate intrinsic and extrinsic camera parameters. In an experiment, we confirmed that the proposed method can achieve accurate camera parameter estimation during camera zooming.

The development of the use of non-metric digital cameras in the form of action cameras for collecting geospatial data has become very useful and supports the work of mobile mapping for making three-dimensional (3D) models. Each lens has an error in the formation of a projection design and also an error during production. For example in a fisheye lens, which has a distortion model, namely radial distortion, tangential distortion, and shifting of the optical center point. The camera is considered to be calibrated if the principal distance, principal point offset, and lens distortion parameters are known. The preparation stage that needs to be done on the mobile mapping work in making 3D models is camera calibration. This research aims to determine the value of internal orientation parameters of a digital camera (action camera) that is used for mobile mapping purposes. Camera calibration in Photogrammetry aims to determine the geometric model of the camera described by Interior Orientation Parameters (IOP), including focal length, shifting principle point (PP), distortion, and other parameters. The calibration method used is the test field calibration. The calibration activities carried out on digital cameras are by measuring targets in the field using coded targets from Agisoft software. The calibration process is also carried out when processing photo data with Agisoft Photoscan Professional software. Camera calibration results using bundle adjustment on Agisoft Photoscan Professional software produce IOP (Interior Orientation Parameter) parameters, namely principal distance (C), principal point offset (Xp, Yp), and lens distortion parameters (K1, K2, K3, P1 , P2, B1, B2). Based on the results obtained, it can be concluded that Maximum Observational Radial Distance Encountered is 1 mm.

Camera pose estimation under dynamic intrinsic parameter change for augmented reality

Real-Time Camera Calibration for Virtual Studio

Camera calibration is essential for various vision-based 3D metrological techniques. In this paper, a novel camera calibration method, to the best of our knowledge, combining synthetic speckle pattern and an improved gray wolf optimizer algorithm is presented. The synthetic speckle pattern serves as the calibration target. The particle swarm algorithm-based digital image correlation is employed to achieve matches among 3D control points and 2D image points; then the improved gray wolf optimizer algorithm is used to calculate the camera parameters. For verification, simulated and real tests are conducted. Through the analysis of calibration results, the proposed method performs better and is more stable than other calibration targets. Research on the influence of camera pose and optimization algorithm is conducted, showing that the improved gray wolf optimizer algorithm performs better than other benchmark algorithms. The camera parameters can be obtained through one captured image when the speckle patterns are added in the portion of the camera sensor.