This paper focuses on the opportunity to use multiple star trackers to help space situational awareness and space surveillance. Catalogs of space debris around Earth are usually based on ground-based measurements, which rely on data provided by ground-based radar observations and ground-based optical observations. However, space-based observations offer new opportunities because they are independent of the weather and the circadian rhythms to which the ground system is subjected. Consequently, space-based optical observations improve the possibility of space debris detection and cataloging. This work deals with a feasibility study of an innovative strategy, which consists of the use of a star sensor with a dedicated algorithm to run directly on board. This approach minimizes the impact on the original mission of the satellite, and on this basis, it has also the function of space debris monitoring. Therefore, theoretically, every satellite with a star tracker can be used as a space surveillance observer. In this paper, we propose a multi-star space debris detecting and positioning method with constant geocentric observation. Using the multi-star tracker joint positioning method, the angle measurement data of the star tracker is converted into the spatial coordinates of the target. In addition, the Gaussian MMSE difference correction algorithm is used to realize the target positioning of multiple optical observations, and the spatial target position information of the multi-frame images is fused, thus completing the solution of the orbit equation. The simulation results show that the proposed method is sufficient to detect and position space debris. It also demonstrates the necessity and feasibility of cooperative network observation by multiple star trackers.

Eye tracking technology has become increasingly important for psychological analysis, medical diagnosis, driver assistance systems, and many other applications. Various gaze-tracking models have been established by previous researchers. However, there is currently no near-eye display system with accurate gaze-tracking performance and a convenient user experience. In this paper, we constructed a complete prototype of the mobile gaze-tracking system ‘Etracker’ with a near-eye viewing device for human gaze tracking. We proposed a combined gaze-tracking algorithm. In this algorithm, the convolutional neural network is used to remove blinking images and predict coarse gaze position, and then a geometric model is defined for accurate human gaze tracking. Moreover, we proposed using the mean value of gazes to resolve pupil center changes caused by nystagmus in calibration algorithms, so that an individual user only needs to calibrate it the first time, which makes our system more convenient. The experiments on gaze data from 26 participants show that the eye center detection accuracy is 98% and Etracker can provide an average gaze accuracy of 0.53° at a rate of 30–60 Hz.

Strabismus is a common vision disease that brings about unpleasant influence on vision, as well as life quality. A timely diagnosis is crucial for the proper treatment of strabismus. In contrast to manual evaluation, well-designed automatic evaluation can significantly improve the objectivity, reliability, and efficiency of strabismus diagnosis. In this study, we have proposed an innovative intelligent evaluation system of strabismus in digital videos, based on the cover test. In particular, the video is recorded using an infrared camera, while the subject performs automated cover tests. The video is then fed into the proposed algorithm that consists of six stages: (1) eye region extraction, (2) iris boundary detection, (3) key frame detection, (4) pupil localization, (5) deviation calculation, and (6) evaluation of strabismus. A database containing cover test data of both strabismic subjects and normal subjects was established for experiments. Experimental results demonstrate that the deviation of strabismus can be well-evaluated by our proposed method. The accuracy was over 91%, in the horizontal direction, with an error of 8 diopters; and it was over 86% in the vertical direction, with an error of 4 diopters.

This paper describes the multilayer voting algorithm, a novel autonomous star identification method for spacecraft attitude determination. The proposed algorithm includes two processes: an initial match process and a verification process. In the initial match process, a triangle voting scheme is used to acquire candidates of the detected stars, in which the triangle unit is adopted as the basic voting unit. During the identification process, feature extraction is implemented, and each triangle unit is described by its singular values. Then the singular values are used to search for candidates of the imaged triangle units, which further improve the efficiency and robustness of the algorithm. After the initial match step, a verification method is applied to eliminate incorrect candidates from the initial results and then outputting the final match results of the imaged stars. Experiments show that our algorithm has more robustness to position noise, magnitude noise, and false stars than the other three algorithms, the identification speed of our algorithm is largely faster than the geometric voting algorithm and optimized grid algorithm. However, it takes more memory, and SVD also seems faster.

With the increasing number of human space activities, space surveillance systems need to be developed to reduce the risk of collisions between space assets and space debris. In this context, optical surveillance systems have gradually become a significant means of space surveillance due to their various advantages. Generally, the sidereal tracking mode is used to search for unknown moving targets, which appear as streaks in the star image generated by the optical surveillance system. Typical matched filtering can detect faint streak-like targets in star images, but it generates more false alarms and must traverse all potential filters. In this paper, the layering approach is used to improve the environment for detecting faint targets, in which dual-threshold segmentation is proposed to separate bright objects while maintaining the completeness of faint targets. Second, a streak-like matched filter unit and a dual-step search approach are recommended to lower the computational cost of matched filtering. Finally, perpendicular cross filtering is provided to further eliminate false positives. Experiments performed with both simulated and real data demonstrate that the proposed method has excellent detection performance for detecting multiple faint streak-like targets in a single star image.

Imaging past the diffraction limit is of significance to an optical system. Fourier ptychography (FP) is a novel coherent imaging technique that can achieve this goal and it is widely used in microscopic imaging. Most phase retrieval algorithms for FP reconstruction are based on Gaussian measurements which cannot extend straightforwardly to long range, sub-diffraction imaging setup because of laser speckle noise corruption. In this work, a new FP reconstruction framework is proposed for macroscopic visible imaging. When compared with existing research, the reweighted amplitude flow algorithm is adopted for better signal modeling, and the Regularization by Denoising (RED) scheme is introduced to reduce the effects of speckle. Experiments demonstrate that the proposed method can obtain state-of-the-art recovered results on both visual and quantitative metrics without increasing computation cost, and it is flexible for real imaging applications.