Accurate camera intrinsic parameters calibration using virtual stereo board

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.

Large-scale stereo vision sensor is of great importance in the measurement for large free-form surface. The intrinsic parameters of cameras and the structure parameters of the stereo vision sensor should be calibrated beforehand. Traditional methods are mainly based on planar and 3D targets which are expensive and difficult to manufacture, especially for large dimension ones. A calibration method for stereo vision sensor based on one-dimensional targets is proposed. First random place two 1D targets, and acquire multiple images of the targets from different angles of view with camera. Solve the intrinsic parameters of camera with the constraint that the spatial angle of the two ID target are constant. Then set up the stereo vision sensor with two calibrated cameras, and acquire multiple images of a 1D target of unknown motion. Based on the constraint of the known distance between two feature points on the target, estimate the initial value of the structure parameters with linear method and the precise structure parameters of stereo vision sensor with non-linear optimization method by setting up the minimizing function involving the scale factors. Experimental results show that, the measurement precision of the stereo vision sensor is 0.052mm, with the working distance of 3500mm and the measurement scale of 4000mm × 3000mm. The method proposed is proved to be suitable for field calibration of stereo vision sensor in application of large-scale measurement for its easy operation and high efficiency.

In this paper, we first look at the problem of estimating the transformation between an inertial measurement unit (IMU) and a calibrated camera, based on images of planar mirror reflections (IPMR) of arbitrary feature points with unknown positions. Assuming that only the reflection of the feature points are observable by the camera, the IMU-camera calibration parameters and the position of the feature points in the camera frame are estimated using the Sigma-Point Kalman filter framework. In the next step, we consider the case of estimating varying camera intrinsic parameters using the estimated static parameters from the previous stage. Therefore, the estimated parameters are used as initial values in the state space model of the system to estimate the camera intrinsic parameters together with the rest of the parameters. The proposed method does not rely on using a fixed calibration pattern whose feature points' positions are known relative to the navigation frame. Additionally, the motion of the camera, which is mounted on the IMU, is not limited to be planar with respect to the mirror. Instead, the reflection of the feature points with unknown positions in the camera body frame are tracked over time. Simulation results show subcentimeter and subdegree accuracy for both IMU-camera translation and rotation parameters as well as submillimeter and subpixel accuracy for the position of the feature points and camera intrinsic parameters, respectively.

A new method for calibrating stereo vision measurement system is presented. By moving a rigid optical reference bar in a pattern in and around the measurement volume, the relative orientation and position of the two cameras can be determined by using the epipolar constraint, the linear normalization eight-point algorithm and the M-estimator method. The scale factor of the translation between the two cameras can be calculated by the distances between the marks on the reference bar. With automatically controlled infrared LED marks, the imaging feature points have a uniform intensity profile and a high contrast with background. The calibration accuracy can be improved. The simulation and experiments have shown that the calibration accuracy can be comparable with the complex off-line calibration.

A fully automatic 3D reconstruction method based on images without manual modeling is presented in this paper. The method extracts 3D information from 2D images directly. Firstly, the feature points must be extracted from the images and then we match these corresponding points. Secondly, to determine the coordinates of 3D points, we should calculate the fundamental matrix, along with the extrinsic and intrinsic parameters of camera, which is to get the essential matrix. Consequently, we can implement the 3D reconstruction of the real scene after establishing the 3D model by Delauny triangulation and texture mapping and rendering in the late stage. Experiments with real images show that the method can reduce the reconstruction time of large-scale virtual scene greatly. It is characterized as efficiency, high stability and robustness.

In this paper, a linear calibration method is proposed for a paracatadioptric camera using the images of two spheres. Two spheres are selected in space, and the two groups of their projection circles on the unit viewing sphere are made to intersect at four points. The quadrilateral consisting of four points is a rectangle, so a group of orthogonal directions can be determined in space to obtain a group of orthogonal vanishing points in the paracatadioptric image plane. Because of the relationship between orthogonal vanishing points and intrinsic camera parameters, the intrinsic parameters of a paracatadioptric camera can be linearly solved by at least five views satisfying the above conditions. First, one estimates the sphere images and their antipodal sphere images. Second, by solving the intersection of the images of two spheres and the intersection of the images of their antipodal spheres, a group of orthogonal vanishing points can be obtained in the image plane. Finally, by taking the relationship between the orthogonal vanishing points and the intrinsic camera parameters as constraint conditions, the intrinsic parameters of the paracatadioptric camera can be obtained. Simulation results and real image data demonstrate the effectiveness of our new algorithms.

The paper proposes a new extraction algorithm for contour lines and feature points from profile images for automatic personal identification. First the authors input a side view of a human head within a dark background under uniform lighting conditions by a CCD camera, then transform the input to a color difference signal image. After enhancing edges of the image using Sobel operators they binarize the luminance level of each pixel and portray a black profile in a white background. Next they differentiate the black profile, binarize the differentiated results, and depict a contour line using a thinning operator. Finally they encode the extracted contour line using Freeman's chain code and, using the encoded data, calculate the digital curvature in concave sections of the contour line and determine the concave feature points. Next they draw straight lines connecting the adjacent feature points and specify the convex feature points on the contour line which are farthest from the straight lines. They can obtain feature points of the profile automatically and reliably.

Aimed at the defects of current traffic accident rapid investigation methods, a traffic accident vision swift reconnaissance system based on coded target is proposed. Make use of the uniqueness and feature station's geometric constraint of coded target, and we can realize automatic recognition and match of coded target and precise space scale constraint. The coded targets distributed in the accident scene are imaged by the CCD camera in several locations and orientations, and the 3D coordinates of space measured feature points are automatically solved through the initial external location and orientation of camera based on co-angular constraint pose estimate algorithm and full-scale bundle adjustment algorithm based on collinear constraint. Therefore the 3D information of the traffic accident scene is swiftly acquired and the traffic accident scene is also reconstructed. The experiment results demonstrate that the proposed method is feasible and flexible.

In this paper, we present a self-calibration strategy to estimate camera intrinsic and extrinsic parameters using the scale-invariant feature transform (SIFT). The accuracy of the estimated parameters depends on how reliably a set of image correspondences is established. The SIFT employed in the self-calibration algorithms plays an important role in accurate estimation of camera parameters, because of its robustness to changes in viewing conditions. Under the assumption that the camera intrinsic parameters are constant, experimental results show that the SIFT-based approach using two images yields more competitive results than the existing Harris corner detector-based approach using two images.

In this paper, a new panoramic image generation algorithm is proposed based on more realistic image formation processes. Perspective projection, lens distortion, vignetting and illumination effects are incorporated into the proposed panoramic modeling. Intrinsic and extrinsic camera parameters are estimated by the proposed stable camera parameter estimation algorithm derived from panning camera constraints. This paper shows that accurate panoramic images can be reconstructed based on the proposed camera modeling and parameters estimation. The effectiveness of the proposed algorithm is also shown with several image sequences in terms of reconstruction error from the generated panoramic image.

Due to increasing security concerns, a complete security system should consist of two major components, a computer-based face-recognition system and a real-time automated video surveillance system. A computerbased face-recognition system can be used in gate access control for identity authentication. In recent studies, multispectral imaging and fusion of multispectral narrow-band images in the visible spectrum have been employed and proven to enhance the recognition performance over conventional broad-band images, especially when the illumination changes. Thus, we present an automated method that specifies the optimal spectral ranges under the given illumination. Experimental results verify the consistent performance of our algorithm via the observation that an identical set of spectral band images is selected under all tested conditions. Our discovery can be practically used for a new customized sensor design associated with given illuminations for an improved face recognition performance over conventional broad-band images. In addition, once a person is authorized to enter a restricted area, we still need to continuously monitor his/her activities for the sake of security. Because pantilt-zoom (PTZ) cameras are capable of covering a panoramic area and maintaining high resolution imagery for real-time behavior understanding, researches in automated surveillance systems with multiple PTZ cameras have become increasingly important. Most existing algorithms require the prior knowledge of intrinsic parameters of the PTZ camera to infer the relative positioning and orientation among multiple PTZ cameras. To overcome this limitation, we propose a novel mapping algorithm that derives the relative positioning and orientation between two PTZ cameras based on a unified polynomial model. This reduces the dependence on the knowledge of intrinsic parameters of PTZ camera and relative positions. Experimental results demonstrate that our proposed algorithm presents substantially reduced computational complexity and improved flexibility at the cost of slightly decreased pixel accuracy as compared to Chen and Wang’s method [18].

Stereo vision is the key in the visual measurement, robot vision, and autonomous navigation. Before performing the system of stereo vision, it needs to calibrate the intrinsic parameters for each camera and the external parameters of the system. In engineering, the intrinsic parameters remain unchanged after calibrating cameras, and the positional relationship between the cameras could be changed because of vibration, knocks and pressures in the vicinity of the railway or motor workshops. Especially for large baselines, even minute changes in translation or rotation can affect the epipolar geometry and scene triangulation to such a degree that visual system becomes disabled. A technology including both real-time examination and on-line recalibration for the external parameters of stereo system becomes particularly important. This paper presents an on-line method for checking and recalibrating the positional relationship between stereo cameras. In epipolar geometry, the external parameters of cameras can be obtained by factorization of the fundamental matrix. Thus, it offers a method to calculate the external camera parameters without any special targets. If the intrinsic camera parameters are known, the external parameters of system can be calculated via a number of random matched points. The process is: (i) estimating the fundamental matrix via the feature point correspondences; (ii) computing the essential matrix from the fundamental matrix; (iii) obtaining the external parameters by decomposition of the essential matrix. In the step of computing the fundamental matrix, the traditional methods are sensitive to noise and cannot ensure the estimation accuracy. We consider the feature distribution situation in the actual scene images and introduce a regional weighted normalization algorithm to improve accuracy of the fundamental matrix estimation. In contrast to traditional algorithms, experiments on simulated data prove that the method improves estimation robustness and accuracy of the fundamental matrix. Finally, we take an experiment for computing the relationship of a pair of stereo cameras to demonstrate accurate performance of the algorithm.

The process of solving camera intrinsic and extrinsic parameters has been presented by using geometric techniques. In contrast with traditional method, which is a pure algebra process, the geometric technique accomplishes the camera parameters more intuitively and easily. If the perspective projection matrix is given, according to the geometric meaning of perspective projection camera model, the coordinates of optical centre can be directly estimated in the world coordinate system. Then rotation matrix and translation vector can be obtained from the matrix. Finally, we can derive camera intrinsic parameters. During the process of derivation, for any given 3 by 4 matrix, the constraint conditions have been discussed in another way when the matrix could be described as a perspective projection transformation. Although it has the same result as pure algebra progress, it's of great significance to understand the geometric meaning among the intrinsic and extrinsic camera parameters

This study addressed the critical challenge of systematic alignment errors in freeform surface measurement, which arose from spatial deviations between design and measurement coordinate systems. An enhanced Coherent Point Drift (CPD) registration method incorporating 3D-Harris feature point extraction was proposed. The methodology employed the 3D-Harris operator to extract local curvature feature points, significantly reducing data volume while preserving essential geometric characteristics. An initial coarse registration was achieved through coordinate transformation of the feature point set, followed by precision fine registration using the CPD algorithm enhanced with local structural constraints. Comprehensive experiments using the standard Bunny model and freeform surface measurement data demonstrated the superior performance of the proposed method. Compared to conventional CPD algorithms, the method achieved reductions of 34% in peak-to-valley (PV) error and 31% in root-mean-square (RMS) error on the Bunny model, along with a 77% reduction in computation time. For freeform surfaces, the method attained exceptional precision with PV = 11 nm and RMS = 7 nm registration errors, while maintaining high efficiency with a processing time of approximately 1.4 seconds. These results confirmed the effectiveness of the proposed approach in achieving high-precision and high-efficiency point cloud registration for freeform surfaces.

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