Three-dimensional (3D) reconstruction using line structured light vision system commonly cooperates with motion restraint devices, such as parallel guide rail push-broom devices. In this study, we propose a visual positioning method to eliminate the motion constraint. An extended orthogonal iteration algorithm for visual positioning is proposed to obtain the precise position of the line structured light binocular camera system during movement. The algorithm uses the information acquired by the binocular camera, and produces a better positioning accuracy than the traditional vision localization algorithm. Furthermore, a global optimization method is proposed to calculate the poses of the camera relative to the world coordinate system at each shooting position. This algorithm effectively reduces the error accumulation and pose drift during visual positioning, and 3D information of the surface can be measured via the proposed free-moving line structured light vision system. The simulation and physical experiments performed herein validate the proposed method and demonstrate the significant improvement in the reconstruction accuracy: when the test distance is 1.5 m, the root mean square error of the point cloud is within 0.5 mm.

Multi-camera systems are widely used in the fields of airborne remote sensing and unmanned aerial vehicle imaging. The measurement precision of these systems depends on the accuracy of the extrinsic parameters. Therefore, it is important to accurately calibrate the extrinsic parameters between the onboard cameras. Unlike conventional multi-camera calibration methods with a common field of view (FOV), multi-camera calibration without overlapping FOVs has certain difficulties. In this paper, we propose a calibration method for a multi-camera system without common FOVs, which is used on aero photogrammetry. First, the extrinsic parameters of any two cameras in a multi-camera system is calibrated, and the extrinsic matrix is optimized by the re-projection error. Then, the extrinsic parameters of each camera are unified to the system reference coordinate system by using the global optimization method. A simulation experiment and a physical verification experiment are designed for the theoretical arithmetic. The experimental results show that this method is operable. The rotation error angle of the camera’s extrinsic parameters is less than 0.001rad and the translation error is less than 0.08 mm.

This paper proposes a 3D point-of-regard estimation method based on 3D eye model and a corresponding head-mounted gaze tracking device. Firstly, a head-mounted gaze tracking system is given. The gaze tracking device uses two pairs of stereo cameras to capture the left and right eye images, respectively, and then sets a pair of scene cameras to capture the scene images. Secondly, a 3D eye model and the calibration process are established. Common eye features are used to estimate the eye model parameters. Thirdly, a 3D point-of-regard estimation algorithm is proposed. Three main parts of this method are summarized as follows: (1) the spatial coordinates of the eye features are directly calculated by using stereo cameras; (2) the pupil center normal is used to the initial value for the estimation of optical axis; (3) a pair of scene cameras are used to solve the actual position of the objects being watched in the calibration process, and the calibration for the proposed eye model does not need the assistance of the light source. Experimental results show that the proposed method can output the coordinates of 3D point-of-regard more accurately.

This study is intended for modeling and calibration of a precise optical positioning system for tracking 3D positions of remote targets in a large space. This system is made up of four linear cameras, which are equipped with cylindrical lenses. The four cameras are paired up as two identical groups. Each camera group is composed of two linear cameras that are packaged together with their imaging orientations normal to each other. The specially designed structure makes the system superior to existing three-linear-CCD-camera systems used for position tracking, in the efficiency of eliminating distortion of cylindrical lenses, a long-standing problem in precise calibration of linear cameras with cylindrical lenses. During the modeling and calibration process, each camera group is treated as an integrated 2D image sensor. A complete imaging model is established for each camera group, and the object-space error is used in calibration for obtaining optimal camera parameters. Simulative and real experiments have verified that, when the two cameras in each group have a good distortion consistency, the proposed calibration approach can effectively fit the model of linear cameras and correct the distortion of cylindrical lenses, thus leading to a significant improvement of positioning accuracy.

With the introduction of deep learning, a wave of artificial intelligence research has been set off again. Scientists focus on brain-inspired intelligence, namely, try to get inspiration from the brain nervous system and cognitive behavior mechanism, to develop intelligent computing models as well as algorithms with stronger information representation, processing and learning ability. So, the study of neurons and the connections between neurons of brain are needed. One major obstacle of reconstruction lies in segmenting and tracking neuronal processes. Electron microscopy is producing neurons images rapidly. In response, we propose an efficient method for neuronal tracking in electron microscopy images to help scientists reconstruct complex neurons. First, we track neurons by kernelized correlation filter to get candidate neuron; then we calculate overlap area and distance of the contours between two consecutive images to get final neuron. We evaluate the performance of our method on a public electron microscopy dataset. The method is superior in accuracy and efficiency.