Video Camera Calibration Research Articles

Real‐time fusion of multiple videos and 3D real scenes based on optimal viewpoint selection

Abstract3D real scenes are a digital virtual space that represents and portrays the real world in a photorealistic, three‐dimensional, and sequential manner. Existing methods for constructing and updating 3D models, such as oblique photography and laser scanning, are difficult to meet the demand of perceiving the real world intuitively, dynamically, and in real time. In recent years, the method of integrating rapidly rising video data and 3D models has become increasingly popular. Compared with existing methods, it enhances the real‐time perception of 3D scenes by taking advantage of the real‐time character of videos and the intuitive character of 3D models. In this article, we propose a real‐time fusion method of multiple videos and 3D real scenes based on optimal viewpoint selection. To begin, 3D reconstruction and video camera calibration were used to prepare the basic data for the fusion of videos and 3D model. Second, a visible‐surface detection‐based video space restoration method was provided, and the overlapping region between multiple videos was determined. Third, to split the overlapping region into the corresponding camera spaces, a segmentation method based on optimal viewpoint selection was given. Finally, the 2D videos were dynamically fitted to the 3D model using the dynamic texture mapping method, while accomplishing the fusing and rendering of the 3D real scene. After experimental verification, the overall effect of the multiple videos and 3D real scene fusion system implemented using the method proposed in this article is better, while the algorithm is less time‐consuming and efficient in rendering.

Time-series methods in analysis of the optical tweezers recordings.

In this paper we treat optical tweezers as discrete-time linear filters and analyze the recorded trajectories of the trapped beads using time-series methods. Using these techniques we obtain a simple analytical formula for the aliased power-spectrum density. Moreover, we separate influences of the noise and blur induced by the video camera from the physical content of the measurements, providing simple tools to detect and account for these distortions. Finally, checking how our tools work on the real data, we identify what parameters of video camera calibration the blur is dominating and what the additive noise is dominating. We also detect a range where these two distortions cancel each other so that the data can be mistakenly classified as undisturbed.

Improvement on Zhang's Camera Calibration

This paper presents an improved initial guessing method for the famous Zhang's camera calibration method for a moving camera. Given three or more known points in a scene, we can compute initial guessing values for maximum likelihood estimation for varying focal camera calibration. This method is useful in structure from motion, which needs consecutive video camera calibration using a known camera distortion.

3D modeling of architectural objects from video data obtained with the fixed focal length lens geometry

Abstract The article describes the process of creating 3D models of architectural objects on the basis of video images, which had been acquired by a Sony NEX-VG10E fixed focal length video camera. It was assumed, that based on video and Terrestrial Laser Scanning data it is possible to develop 3D models of architectural objects. The acquisition of video data was preceded by the calibration of video camera. The process of creating 3D models from video data involves the following steps: video frames selection for the orientation process, orientation of video frames using points with known coordinates from Terrestrial Laser Scanning (TLS), generating a TIN model using automatic matching methods. The above objects have been measured with an impulse laser scanner, Leica ScanStation 2. Created 3D models of architectural objects were compared with 3D models of the same objects for which the self-calibration bundle adjustment process was performed. In this order a PhotoModeler Software was used. In order to assess the accuracy of the developed 3D models of architectural objects, points with known coordinates from Terrestrial Laser Scanning were used. To assess the accuracy a shortest distance method was used. Analysis of the accuracy showed that 3D models generated from video images differ by about 0.06 ÷ 0.13 m compared to TLS data.

A variational approach to camera motion smoothing

In this paper we study a variational problem derived from a computer vision application: video camera calibration with smoothing constraint. By video camera calibration we mean to estimate the location, orientation and lens zoom-setting of the camera for each video frame taking into account image visible features. To simplify the problem we assume that the camera is mounted on a tripod, in such case, for each frame captured at time t , the calibration is provided by 3 parameters : (1) P(t) (PAN) which represents the tripod vertical axis rotation, (2) T (t) (TILT) which represents the tripod horizontal axis rotation and (3) Z(t) (CAMERA ZOOM) the camera lens zoom setting. The calibration function t → u(t) = (P(t),T (t),Z(t)) is obtained as the minima of an energy function I[u] . In this paper we study the existence of minima of such energy function as well as the solutions of the associated Euler-Lagrange equations.