As a novel X-ray focusing technology, lobster-eye micropore optics (MPO) feature both a wide observing field of view and true imaging capability, promising sky monitoring with significantly improved sensitivity and spatial resolution in soft X-rays. Since first proposed by Angel, the optics have been extensively studied, developed and trialed over the past decades. In this Letter, we report on the first-light results from a flight experiment of the Lobster Eye Imager for Astronomy, a pathfinder of the wide-field X-ray telescope of the Einstein Probe mission. The piggyback imager, launched in 2022 July, has a mostly unvignetted field of view of 18.°6 × 18.°6. Its spatial resolution is in the range of 4′–7′ in FWHM and the focal spot effective area is 2–3 cm², both showing only mild fluctuations across the field of view. We present images of the Galactic center region, Sco X-1, and the diffuse Cygnus Loop nebular taken in snapshot observations over 0.5–4 keV. These are truly wide-field X-ray images of celestial bodies observed, for the first time, by a focusing imaging telescope. Initial analyses of the in-flight data show excellent agreement between the observed images and the on-ground calibration and simulations. The instrument and its characterization are briefly described, as well as the flight experiment. The results provide a solid basis for the development of the present and proposed wide-field X-ray missions using lobster-eye MPO.

Rigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the corresponding point and lead to sensitivity to the outlier and initial pose, while they have poor computational efficiency due to the closest point computation. Most implementations of the ICP algorithm attempt to deal with this issue by modifying correspondence or adding coarse registration. However, this leads to sacrificing the accuracy rate or adding the algorithm complexity. This paper proposes a hierarchical optimization approach that includes improved voxel filter and Multi-Scale Voxelized Generalized-ICP (MVGICP) for 3D point cloud registration. By combining traditional voxel sampling with point density, the outlier filtering and downsample are successfully realized. Through multi-scale iteration and avoiding closest point computation, MVGICP solves the local minimum problem and optimizes the operation efficiency. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of outlier filtering and registration performance.

The rapid development of machine vision has prompted the continuous emergence of new detection systems and algorithms in surface defect detection. However, most of the existing methods establish their systems with few comparisons and verifications, and the methods described still have various problems. Thus, an original defect detection method: Segmented Embedded Rapid Defect Detection Method for Surface Defects (SERDD) is proposed in this paper. This method realizes the two-way fusion of image processing and defect detection, which can efficiently and accurately detect surface defects such as depression, scratches, notches, oil, shallow characters, abnormal dimensions, etc. Besides, the character recognition method based on Spatial Pyramid Character Proportion Matching (SPCPM) is used to identify the engraved characters on the bearing dust cover. Moreover, the problem of characters being cut in coordinate transformation is solved through Image Self-Stitching-and-Cropping (ISSC). This paper adopts adequate real image data to verify and compare the methods and proves the effectiveness and advancement through detection accuracy, missing alarm rate, and false alarm rate. This method can provide machine vision technical support for bearing surface defect detection in its real sense.

The lobster eye telescope is promising for large-field x ray imaging in astronomy. The special structure of the lobster eye system makes the focal plane a sphere, resulting in detector defocus when the field is large. In this study, we established a model based on the principle of lobster eye imaging and simulated the imaging at different image distances. The results reveal the relationship between the defocus and position accuracy and angular resolution. To ensure the optical performance of the large field lobster eye telescope, we propose a detection system design method using multiple detectors stitched together to form a spherical-like surface and apply it to the development of the Einstein Probe/wide-field x ray telescope (EP/WXT) submodule. About 70% of the detection area is out of focus within 0.5 mm. The scanning image of the integrated WXT submodule shows good uniformity of the point spread function (PSF) for various incident angles, and the effect of defocus on imaging is acceptable.

Recent years have witnessed the widespread research of the surface defect detection technology based on machine vision, which has spawned various effective detection methods. In particular, the rise of deep learning has allowed the surface defect detection technology to develop further. However, these methods based on deep learning still have some drawbacks. For example, the size of the sample data is not large enough to support deep learning; the location and recognition of surface defects are not accurate enough; the real-time performance of segmentation and classification is not satisfactory. In the context, this paper proposes an end-to-end convolutional neural network model: the pixel-wise segmentation and image-wise classification network (PSIC-Net). With the innovative design of a three-stage network structure, improved loss function and a two-step training mode, PSIC-Net can accurately and quickly segment and classify surface defects with a small dataset of training data. This model was evaluated with three public datasets, and compared with the most advanced defect detection methods. All the performance metrics prove the effectiveness and advancement of PSIC-Net.

Egocentric activity recognition in first-person video (FPV) requires fine-grained matching of the camera wearer’s action and the objects being operated. The traditional method used for third-person action recognition does not suffice because of (1) the background ego-noise introduced by the unstructured movement of the wearable devices caused by body movement; (2) the small-sized and fine-grained objects with single scale in FPV. Size compensation is performed to augment the data. It generates a multi-scale set of regions, including multi-size objects, leading to superior performance. We compensate for the optical flow to eliminate the camera noise in motion. We developed a novel two-stream convolutional neural network-recurrent attention neural network (CNN-RAN) architecture: spatial temporal attention on compensation information (STAC), able to generate generic descriptors under weak supervision and focus on the locations of activated objects and the capture of effective motion. We encode the RGB features using a spatial location-aware attention mechanism to guide the representation of visual features. Similar location-aware channel attention is applied to the temporal stream in the form of stacked optical flow to implicitly select the relevant frames and pay attention to where the action occurs. The two streams are complementary since one is object-centric and the other focuses on the motion. We conducted extensive ablation analysis to validate the complementarity and effectiveness of our STAC model qualitatively and quantitatively. It achieved state-of-the-art performance on two egocentric datasets.