<p>We propose an automatic calibration method using grid-structured light for the full parameter of a camera-projector system, including principal points, equivalent focal length, image distortion coefficients, the rotation matrix, and translation vectors between the camera and projector. Grid-structured light is projected onto a board, camera image intersection points are extracted, and three-dimensional intersection points are computed according to a homography matrix. Finally, the full parameter of a camera-projector system is solved based on stereo vision. No manual intervention is required during image processing, which simplifies the operations and improves efficiency. The image-processing kernel problem involves both automatic detection and intersection point matching. (1) The proposed intersection point detection method utilizes multiscale fusion. At each level of the image pyramid, intersection points are searched according to gray distribution and geometrical characteristics. With the gray-gravity method, coordinates are achieved with subpixel intersection point precision. Therefore, the location precision exceeds 0.5 pixel. (2) The proposed matching method employs belief propagation. Taking intersection points as nodes, a Bayesian network is established according to the Markov random field hypothesis. The image intersection point matching problem between a camera and the projector is then transformed into a maximum <italic>a posteriori</italic> estimation problem. Ultimately, 15 images are used to calibrate the full parameter of a camera-projector system. The results indicate that the reprojection error exceeds 0.15 pixel.</p>
Our primary interest is in real-time one-dimensional object’s pose estimation. In this paper, a method to estimate general motion one-dimensional object’s pose, that is, the position and attitude parameters, using a single camera is proposed. Centroid-movement is necessarily continuous and orderly in temporal space, which means it follows at least approximately certain motion law in a short period of time. Therefore, the centroid trajectory in camera frame can be described as a combination of temporal polynomials. Two endpoints on one-dimensional object, A and B, at each time are projected on the corresponding image plane. With the relationship between A, B and centroid C, we can obtain a linear equation system related to the temporal polynomials’ coefficients, in which the camera has been calibrated and the image coordinates of A and B are known. Then in the cases that object moves continuous in natural temporal space within the view of a stationary camera, the position of endpoints on the one-dimensional object can be located and also the attitude can be estimated using two end points. Moreover the position of any other point aligned on one-dimensional object can also be solved. Scene information is not needed in the proposed method. If the distance between the endpoints is not known, a scale factor between the object’s real positions and the estimated results will exist. In order to improve the algorithm’s performance from accuracy and robustness, we derive a pain of linear and optimal algorithms. Simulations’ and experiments’ results show that the method is valid and robust with respect to various Gaussian noise levels. The paper’s work contributes to making self-calibration algorithms using one-dimensional objects applicable to practice. Furthermore, the method can also be used to estimate the pose and shape parameters of parallelogram, prism or cylinder objects.
The mobile measurement equipment plays an important role in engineering measurement tasks and its measuring device is fixed with the vehicle platform. Therefore, how to correct the measured error in time that caused by swayed platform is a basic problem. Videometrics has its inherent advantages in solving this problem. First of all, videometrics technology is non-contact measurement, which has no effect on the target’s structural characteristics and motion characteristics. Secondly, videometrics technology has high precision especially for surface targets and linear targets in the field of view. Thirdly, videometrics technology has the advantages of automatic, real-time and dynamic. This paper is mainly for mobile theodolite.etc that works under the environment of absolute vertical benchmark and proposed two high-precision methods to determine vertical benchmark: Direct-Extracting, which is based on the intersection of plats under the help of two cameras; Benchmark-Transformation, which gets the vertical benchmark by reconstructing the level-plat. Two methods both have the precision of under 10 seconds by digital simulation and physical experiments. The methods proposed by this paper have significance both on the theory and application.