Self-Calibration Spherical Video Stabilization Based on Gyroscope

With the development of handheld video capturing devices, video stabilization becomes increasingly important. The gyroscope-based video stabilization methods perform promising ability, since they can return more reliable three-dimensional (3D) camera rotation estimation, especially when there are many moving objects in scenes or there are serious motion blur or illumination changes. However, the gyroscope-based methods depend on the camera intrinsic parameters to execute video stabilization. Therefore, a self-calibrated spherical video stabilization method was proposed. It builds a virtual sphere, of which the spherical radius is calibrated automatically, and then projects each frame of the video to the sphere. Through the inverse rotation of the spherical image according to the rotation jitter component, the dependence on the camera intrinsic parameters is relaxed. The experimental results showed that the proposed method does not need to calibrate the camera and it can suppress the camera jitter by binding the gyroscope on the camera. Moreover, compared with other state-of-the-art methods, the proposed method can improve the peak signal-to-noise ratio, the structural similarity metric, the cropping ratio, the distortion score, and the stability score.

Rapid Calibration Method for 3D Laser Scanner

Background: The 3D laser scanner is a non-contact active-sensing system, which has a number of applications. Many patents have been filed on the technologies for calibrating 3D laser scanner. A precise calibration method is important for measuring the accuracy of the 3D laser scanner. The system model contains three categories of parameters to be calibrated which include the camera intrinsic parameters, distortion coefficients and the light plane parameters. Typically, the calibration process is completed in two steps. Based on Zhang’s method, the calibration of the camera intrinsic parameters and distortion coefficients can be performed. Then, 3D feature points on the light plane should precisely be formed and extracted. Finally, the points are used to calculate the light plane parameters. Methods: In this paper, a rapid calibration method is presented. Without any high precision auxiliary device, only one coplanar reference target is used. By using a group of captured images of the coplanar reference target placed in the field of view arbitrarily, calibration can be performed in one step. Based on the constraint from the planes formed by the target in different directions and the camera imaging model, a large amount of 3D points on the light plane can easily be obtained. The light plane equation in the camera coordinates system can be gathered by executing plane fitting to the 3D points. Results: During the experimental process, the developed 3D laser scanner was calibrated by the proposed method. Then, the measuring accuracy of the system was verified with known distance in vertical direction of 1mm with sequential shifting motion generated by precision translation stage. The average value of the measured distances was found to be 1.010mm. The standard deviation was 0.008mm. Conclusion: Experimental results prove that the proposed calibration method is simple and reliable.

An efficient parameter update method of 360-degree VR image model

Recently, with the rapid growth of manufacture and ease of user convenience, technologies utilizing virtual reality images have been increasing. It is very important to estimate the projected direction and position of the image to show the image quality similar to the real world, and the estimation of the direction and the position is solved using the relation that transforms the spheres into the expanded equirectangular. The transformation relationship can be divided into a camera intrinsic parameter and a camera extrinsic parameter, and all the images have respective camera parameters. Also, if several images use the same camera, the camera intrinsic parameters of the images will have the same values. However, it is not the best way to set the camera intrinsic parameter to the same value for all images when matching images. To solve these problems and show images that does not have a sense of heterogeneity, it is needed to create the cost function by modeling the conversion relation and calculate the camera parameter that the residual value becomes the minimum. In this article, we compare and analyze efficient camera parameter update methods. For comparative analysis, we use Levenberg–Marquardt, a parameter optimization algorithm using corresponding points, and propose an efficient camera parameter update method based on the analysis results.

A Decoupled Calibration Method for Camera Intrinsic Parameters and Distortion Coefficients

Camera calibration is a necessary process in the field of vision measurement. In this paper, we propose a flexible and high-accuracy method to calibrate a camera. Firstly, we compute the center of radial distortion, which is important to obtain optimal results. Then, based on the radial distortion of the division model, the camera intrinsic parameters and distortion coefficients are solved in a linear way independently. Finally, the intrinsic parameters of the camera are optimized via the Levenberg-Marquardt algorithm. In the proposed method, the distortion coefficients and intrinsic parameters are successfully decoupled; calibration accuracy is further improved through the subsequent optimization process. Moreover, whether it is for relatively small image distortion or distortion larger image, utilizing our method can get a good result. Both simulation and real data experiment demonstrate the robustness and accuracy of the proposed method. Experimental results show that the proposed method can be obtaining a higher accuracy than the classical methods.