Matching Points Research Articles

Enhancing photovoltaic performance of antimony sulfide-selenide tandem solar cells through selenium content variation: Modeling and simulation analysis

In this study, we utilized simulations and modeling to examine the effect of selenium content on the performance of antimony sulfide-selenide tandem solar cells. By manipulating the thickness and band gap of each sub-cell, we identified the current matching point, which is crucial for achieving equal current levels in both sub-cells. Our results emphasize the significance of maintaining a higher energy band gap in the top cell than in the bottom cell for efficient performance. We observed that an increase in the difference in selenium quantity between the upper and lower cells led to an increase in the efficiency value of the tandem cell, with cell current being the predominant factor governing efficacy. These findings will contribute to the development of more efficient and optimized designs for SbSSe/SbSSe tandem solar cells.

Research of feature matching algorithm on panoramic mosaic

Focusing on the application problem according to feature matching and panoramic image stitching, comparing direct stitching and smoothing after feature matching algorithms. Through time performance, synthesis accuracy, and synthesis efficiency, comprehensively compared their effects of synthesizing panoramic images to find a better solution for panoramic splicing. In the study, there are three groups of image pairs with parallax and brightness contrast, then use SIFT algorithm to conduct feature point and matching feature point. The RANSAC algorithm obtains the homograph matrix and completes the panoramic image direct splicing and smoothing test through the matrix transformation. Because of the luminance difference, the panoramic picture after direct splicing will show more obvious light and dark. Though the smoothing treatment could be performed rigidly in the light and dark difference, the deformation, and heavy shadow are easier produced when the disparity and brightness difference are large.

Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution

The key to image depth estimation is to accurately find corresponding points between the left and right images. A binocular camera can directly estimate the depth of the left and right image range, which completely avoids the requirement of target recognition accuracy in a monocular depth estimation. However, it is difficult for binocular stereo matching to accurately segment objects and find matching points in the ill-posed areas (weak texture, deformation, object edge, etc.) of the left and right images. In the semantic segmentation task, atrous convolution is used to solve the contradiction between the receptive field and the segmentation accuracy. This research focused on balancing the impact of holes on the segmentation task. In addition, in order to solve the issue where matching points in the ill-posed regions of left and right images are affected by noise, we used a 3D convolution to aggregate the cost volume to obtain better accuracy. However, the 3D convolution method is prone to mismatching in ill-posed areas of the image. To tackle the problems above, we proposed a dense grouping atrous convolution spatial pyramid pooling (DenseGASPP) method. The feature of the DenseGASPP method is that there is a dense connection between the group atrous convolutions to fully integrate feature information. This method can expand the receptive field and balance the effect of holes on the segmentation task. Moreover, we introduced multi-scale cost aggregation into our method, which uses the repeated exchange of information between cost volumes of different scales to obtain rich contextual information and reduce the mismatching of the network. To evaluate the performance of our method, we conducted several groups of typical algorithm experiments on the scene flow and KITTI 2015 standard datasets. From the results, our model achieves better performance, reducing the EPE from 1.09 to 0.67, which improves the mismatching ability of binocular depth estimation algorithm in ill-posed regions.

Partial-to-Partial Point Cloud Registration by Rotation Invariant Features and Spatial Geometric Consistency

Point cloud registration is a critical problem in 3D vision tasks, and numerous learning-based point cloud registration methods have been proposed in recent years. However, a common issue with most of these methods is that their feature descriptors are rotation-sensitive, which makes them difficult to converge at large rotations. In this paper, we propose a new learning-based pipeline to address this issue, which can also handle partially overlapping 3D point clouds. Specifically, we employ rotation-invariant local features to guide the point matching task, and utilize a cross-attention mechanism to update the feature information between the two point clouds to predict the key points in the overlapping regions. Subsequently, we construct a feature matrix based on the features of the key points to solve the soft correspondences. Finally, we construct a non-learning correspondence constraint module that exploits the spatial geometric invariance of the point clouds after rotation and translation, as well as the compatibility between point pairs, to reject the wrong correspondences. To validate our approach, we conduct extensive experiments on ModelNet40. Our approach achieves better performance compared to other methods, especially in the presence of large rotations.

UW Deep SLAM-CNN Assisted Underwater SLAM

Abstract Underwater simultaneous localization and mapping (SLAM) poses significant challenges for modern visual SLAM systems. The integration of deep learning networks within computer vision offers promising potential for addressing these difficulties. Our research draws inspiration from deep learning approaches applied to interest point detection and matching, single image depth prediction and underwater image enhancement. In response, we propose 3D-Net, a deep learning-assisted network designed to tackle these three tasks simultaneously. The network consists of three branches, each serving a distinct purpose: interest point detection, descriptor generation, and depth prediction. The interest point detector and descriptor generator can effectively serve as a front end for a classical SLAM system. The predicted depth information is akin to a virtual depth camera, opening up possibilities for various applications. We provide quantitative and qualitative evaluations to illustrate some of these potential uses. The network was trained in in several steps, using in-air datasets and followed by generated underwater datasets. Further, the network is integrated into feature-based SALM systems ORBSLAM2 and ORBSSLAM3, providing a comprehensive assessment of its effectiveness for underwater navigation.

Hybrid-Type DAB Converter With DC Blocking Capacitor for Ultrawide Input-Voltage Range

In this letter, a hybrid-type dual active bridge topology with dc blocking capacitor for ultra-wide input voltage is proposed. The proposed topology can expand the gain range by introducing voltage offset across the dc blocking capacitor, and has the ability to operate in single-stage or two-stage mode. The control strategy is also designed, which adopts the minimum resistive loss under the premise of soft switching as the optimization object to derive the operating boundary of the optimal modes. The proposed methodology avoids the additional loss caused by the increased devices of the traditional two-stage converters, and also avoids the efficiency dip of traditional converters with dc blocking capacitor at non-optimal voltage matching points. The performance of the proposed scheme is verified by experimental results.

3D thermal mapping of smoldering coal gangue pile fires using airborne thermal infrared data

Smoldering coal gangue pile fires pollute the air, soil and groundwater severely. Physical and chemical explosions between water and fiery coal gangue may occur, resulting in a large number of casualties. Recognizing and delineating the scope of coal gangue pile fires is a precondition and top priority for firefighting efforts. The present study employs a 3D thermal imaging methodology based on Unmanned Aerial Vehicle (UAV) remote sensing data. Thermal infrared oblique photogrammetry is used to collect overlap-high thermal infrared images of smoldering fires. A batch preprocessing software is developed to standardize the temperature range, temperature distribution mode and color scale of all thermal infrared images, improving feature point identification accuracy. The elaborate delineation and accurate identification of 3D temperature field are realized by feature point matching and texture mapping. The method was used to build the 3D temperature model and visualize the scope of the coal gangue pile fires in the 1360 bench, viewing bench and belt corridor of the Anjialing Open-pit Mine in China. The 3D thermal models accurately characterize the distribution of the smoldering fires, providing firm support and guidance for the firefighting efforts.

Research on a Horizon Line Detection Method for Unmanned Surface Vehicles in Complex Environments

A critical step in the visual navigation of unmanned surface vehicles (USVs) is horizon line detection, which can be used to adjust the altitude as well as for obstacle avoidance in complex environments. In this paper, a real-time and accurate detection method for the horizon line is proposed. Our approach first differentiates the complexity of navigational scenes using the angular second moment (ASM) parameters in the grey level co-occurrence matrix (GLCM). Then, the region of interest (ROI) is initially extracted using minimal human interaction for the complex navigation scenes, while subsequent frames are dynamically acquired using automatic feature point matching. The matched ROI can be maximally removed from the complex background, and the Zernike-moment-based edges are extracted from the obtained ROI. Finally, complete sea horizon information is obtained through a linear fitting of the lower edge points to the edge information. Through various experiments carried out on a classical dataset, our own datasets, and that of another previously published paper, we illustrate the significance and accuracy of this technique for various complex environments. The results show that the performance has potential applications for the autonomous navigation and control of USVs.

Simultaneous RGB generation in domain engineered QPM devices using type 0 DFG process

We have theoretically demonstrated the simultaneous generation of red (R), green (G), and blue (B) wavelengths by employing the type-0 quasi-phase-matched (QPM) difference frequency generation (DFG) process in congruent LiNbO3 crystal. Various domain-engineered QPM structures are presented and analysed, aiming to achieve an excellent spectral response. The RGB emissions were observed to vary from 632 nm to 656 nm for red, 504 nm to 532 nm for green, and 449 nm to 484 nm for blue, respectively, for different configurations of input pump, operating temperature, and phase matching periods. A maximum shift of about 10 nm to 20 nm was noticed in the spectral position of the DFG peaks when the operating temperature varies from 25 °C to 60 °C. We have also investigated the effect of pump detuning and random domain fluctuations over the phase matching point on the DFG spectrum. Further, we have addressed the advantages of introducing a phase shifter (PS) domain in the periodically poled lithium niobate (PPLN) to produce excellent equal intensity RGB peaks in a single device for their potential application in a laser-based projection display system.

Effect of surrounding objects in the adapting scene on chromatic adaptation.

Most of the existing chromatic adaptation transforms (CATs) were developed for flat uniform stimuli presented in a uniform background, which substantially simplifies the complexity of the real scene by excluding surrounding objects from the viewing field. The impact of the background complexity, in terms of the spatial properties of the objects surrounding the stimulus, on chromatic adaptation is ignored in most CATs. This study systematically investigated how the background complexity and color distribution affect the adaptation state. Achromatic matching experiments were conducted in an immersive lighting booth, with the illumination varying in chromaticity and the adapting scene varying in surrounding objects. Results show that compared to the uniform adapting field, increasing the scene complexity can significantly improve the degree of adaptation for the Planckian illuminations with low CCT levels. In addition, the achromatic matching points are substantially biased by the color of the surrounding object, implying the interactive effect of the illumination color and the dominant scene color on the adapting white point.

Trinocular camera self-calibration based on spatio-temporal multi-layer optimization

In stereo vision systems with dynamically rotating cameras, the accuracy of camera self-calibration method is reduced due to the interference of space noise and mismatched features. To address this issue, a new self-calibration method for trinocular camera is proposed. Firstly, to obtain the uniformly distributed and high-quality matched feature points required for initial calibration of camera pose, according to the mapping relationship between the three-view feature matching points, a three-view grid feature support estimator is defined, and a three-view ring matching method with double-layer feature closed-loop verification is designed. Then, according to the projection relationship of spatial feature points, a new heterogeneous cross-projection optimization function based on closed-loop features is established, achieving accurate calibration of trinocular camera system. Comparison experiments of multiple scenes verify the effectiveness of the method, particularly in the low-textured scenes, where the average Sampson error ranged between 1.37e-11 and 0.061 pixel. Furthermore, the proposed method achieves higher calibration accuracy than the comparative method, which can improve the robustness of dynamic rotating cameras under spatial noise conditions.

A binocular vision-based underwater object size measurement paradigm: Calibration-Detection-Measurement (C-D-M)

In this study, we propose a binocular vision-based underwater object size measurement paradigm: Calibration-Detection-Measurement (C-D-M). In the C-procedure, we construct an underwater refraction model to calculate basic parameters and refractive parameters of an underwater binocular vision system in waterproof housing. These parameters are key to realising the conversion of 2D pixel coordinates to 3D world coordinates. In the D-procedure, we attempt to address the difficult problem of underwater object detection via multi-feature joint perception, and accurately segment the regions of underwater objects in binocular images. Subsequently, the object regions and 3D reconstruction parameters are input into the M-procedure to measure the sizes of underwater objects. Specifically, a novel contour curve matching strategy is presented instead of dense matching to acquire reliable matching points. Subsequently, a 3D contour curve is reconstructed to calculate the key sizes of objects. Finally, comprehensive evaluations are conducted to demonstrate the effectiveness of our C-D-M paradigm.

TCAD Device Simulation of All-Polymer Solar Cells for Indoor Applications: Potential for Tandem vs. Single Junction Cells.

The utilization of indoor photovoltaics makes it feasible to harvest energy from artificial light sources. Although single-junction indoor photovoltaics have demonstrated exceptional efficacy when using LED lighting, there is still a need for more comprehensive testing of tandem structures. Herein, the first systematic TCAD simulation study on the potential for tandem all-polymer solar cells (all-PSCs) for indoor applications is provided. The presented all-PSCs are based on experimental work in which the top wide bandgap subcell comprises a polymer blend PM7:PIDT, while the bottom narrow bandgap subcell has a polymer blend PM6:PY-IT. Standalone and tandem cells are simulated under AM1.5G solar radiation, and the simulation results are compared with measurements to calibrate the physical models and material parameters revealing PCE values of 10.11%, 16.50%, and 17.58% for the front, rear, and tandem cells, respectively. Next, we assessed the performance characteristics of the three cells under a white LED environment for different color temperatures and light intensities. The results showed a superior performance of the front cell, while a deterioration in the performance was observed for the tandem cell, reflecting in a lower PCE of 16.22% at a color temperature of 2900 K. Thus, an optimized tandem for outdoor applications was not suitable for indoor conditions. In order to alleviate this issue, we propose designing the tandem for indoor lightening by an appropriate choice of thicknesses of the top and bottom absorber layers in order to achieve the current matching point. Reducing the top absorber thickness while slightly increasing the bottom thickness resulted in a higher PCE of 27.80% at 2900 K.

ULL-SLAM: underwater low-light enhancement for the front-end of visual SLAM

Underwater visual simultaneous localization and mapping (VSLAM), which can provide robot navigation and localization for underwater vehicles, is crucial in underwater exploration. Underwater SLAM is a challenging research topic due to the limitations of underwater vision and error accumulation over long-term operations. When an underwater vehicle goes down, it may inevitably enter a low-light environment. Although artificial light sources could help to some extent, they might also cause non-uniform illumination, which may have an adverse effect on feature point matching. Consequently, the capability of feature point extraction-based visual SLAM systems could only sometimes work. This paper proposes an end-to-end network for SLAM preprocessing in an underwater low-light environment to address this issue. Our model includes a low-light enhancement branch specific with a non-reference loss function, which can achieve low-light image enhancement without requiring paired low-light data. In addition, we design a self-supervised feature point detector and descriptor extraction branch to take advantage of self-supervised learning for feature points and descriptors matching to reduce the re-projection error. Unlike other works, our model does not require pseudo-ground truth. Finally, we design a unique matrix transformation method to improve the feature similarity between two adjacent video frames. Comparative experiments and ablation experiments confirm that the proposed method in this paper could effectively enhance the performance of VSLAM based on feature point extraction in an underwater low-light environment.

Deep-Learning-Based Multiview RGBD Sensor System for 3-D Face Point Cloud Registration

Three-dimensional face analysis is now a hot research topic in computer vision. The acquisition of 3D faces requires acquiring multi-view face and obtaining them by point cloud registration. In this paper, a deep learning method is introduced in the multi-view RGBD depth sensor system to acquire matching point pairs of image data. Thus, 3D spatial point pairs are obtained. Then, the initial poses between the point clouds of multi-view faces are obtained by rigid body transformation estimation, and the coarse registration of the point clouds is completed. On this basis, for fine registration of multi-view 3D face point clouds using the Iterative Closest Point (ICP) algorithm, this paper proposes a method to convert partially overlapping point cloud registration into sub-region point cloud registration through facial region positioning. In this letter, the maximum value of RMSE for the experimental results of point cloud coarse registration is 2.26 mm with the mean and standard deviation of 1.18 ± 0.33 mm; the maximum value of RMSE for the experimental results of point cloud fine registration is 1.47 mm with the mean and standard deviation of 0.91 ± 0.14 mm. It is confirmed that the proposed method is very robust and effective for the registration of multi-view 3D face point clouds.

Generalized Stereo Matching Method Based on Iterative Optimization of Hierarchical Graph Structure Consistency Cost for Urban 3D Reconstruction

Generalized stereo matching faces the radiation difference and small ground feature difference brought by different satellites and different time phases, while the texture-less and disparity discontinuity phenomenon seriously affects the correspondence between matching points. To address the above problems, a novel generalized stereo matching method based on the iterative optimization of hierarchical graph structure consistency cost is proposed for urban 3D scene reconstruction. First, the self-similarity of images is used to construct k-nearest neighbor graphs. The left-view and right-view graph structures are mapped to the same neighborhood, and the graph structure consistency (GSC) cost is proposed to evaluate the similarity of the graph structures. Then, cross-scale cost aggregation is used to adaptively weight and combine multi-scale GSC costs. Next, object-based iterative optimization is proposed to optimize outliers in pixel-wise matching and mismatches in disparity discontinuity regions. The visibility term and the disparity discontinuity term are iterated to continuously detect occlusions and optimize the boundary disparity. Finally, fractal net evolution is used to optimize the disparity map. This paper verifies the effectiveness of the proposed method on a public US3D dataset and a self-made dataset, and compares it with state-of-the-art stereo matching methods.

Repeated UAV Observations and Digital Modeling for Surface Change Detection in Ring Structure Crater Margin in Plateau

As UAV technology has been leaping forward, small consumer-grade UAVs equipped with optical sensors are capable of easily acquiring high-resolution images, which show bright prospects in a wide variety of terrains and different fields. First, the crater rim landscape of the Dinosaur Valley ring formation located on the central Yunnan Plateau served as the object of the surface change detection experiment, and two repetitive UAV ground observations of the study area were performed at the same altitude of 180 m with DJI Phantom 4 RTK in the rainy season (P1) and the dry season (P2). Subsequently, the UAV-SfM digital three-dimensional (3D) modeling method was adopted to build digital models of the study area at two points in time, which comprised the Digital Surface Model (DSM), Digital Orthomosaic Model (DOM), and Dense Image Matching (DIM) point cloud. Lastly, a quantitative analysis of the surface changes at the pit edge was performed using the point-surface-body surface morphological characterization method based on the digital model. As indicated by the results, (1) the elevation detection of the corresponding check points of the two DSM periods yielded a maximum positive difference of 0.2650 m and a maximum negative value of −0.2279 m in the first period, as well as a maximum positive difference of 0.2470 m and a maximum negative value of −0.2589 m in the second period. (2) In the change detection of the two DOM periods, the vegetation was 9.99% higher in the wet season than in the dry season in terms of coverage, whereas the bare soil was 10.54% more covered than the wet season. (3) In general, the M3C2-PM distances of the P1 point cloud and the P2 point cloud were concentrated in the interval (−0.2,0.2), whereas the percentage of the interval (−0.1,0) accounted for 26.69% of all intervals. The numerical model of UAV-SfM was employed for comprehensive change detection analysis. As revealed by the result of the point elevation difference in the constant area, the technique can conform to the requirements of earth observation with certain accuracy. The change area suggested that the test area can be affected by natural conditions to a certain extent, such that the multi-source data can be integrated to conduct more comprehensive detection analysis.

Large-scale time-lapse scanning electron microscopy image mosaic using a smooth stitching strategy.

Due to the trade-off between the field of view and resolution of various microscopes, obtaining a wide-view panoramic image through high-resolution image tiles is frequently encountered and demanded in numerous applications. Here, we propose an automatic image mosaic strategy for sequential 2D time-lapse scanning electron microscopy (SEM) images. This method can accurately compute pairwise translations among serial image tiles with indeterminate overlapping areas. The detection and matching of feature points are limited by geographical coordinates, thus avoiding accidental mismatching. Moreover, the nonlinear deformation of the mosaic part is also taken into account. A smooth stitching field is utilized to gradually transform the perspective transformation in overlapping regions into the linear transformation in non-overlapping regions. Experimental results demonstrate that better image stitching accuracy can be achieved compared with some other image mosaic algorithms. Such a method has potential applications in high-resolution large-area analysis using serial microscopy images. RESEARCH HIGHLIGHTS: An automatic image mosaic strategy for processing sequential scanning electron microscopy images is proposed. A smooth stitching field is applied in the image mosaic. Improved stitching accuracy is achieved compared with other conventional mosaic methods.

The big warp: Registration of disparate retinal imaging modalities and an example overlay of ultrawide-field photos and en-face OCTA images.

To develop an algorithm and scripts to combine disparate multimodal imaging modalities and show its use by overlaying en-face optical coherence tomography angiography (OCTA) images and Optos ultra-widefield (UWF) retinal images using the Fiji (ImageJ) plugin BigWarp. Optos UWF images and Heidelberg en-face OCTA images were collected from various patients as part of their routine care. En-face OCTA images were generated and ten (10) images at varying retinal depths were exported. The Fiji plugin BigWarp was used to transform the Optos UWF image onto the en-face OCTA image using matching reference points in the retinal vasculature surrounding the macula. The images were then overlayed and stacked to create a series of ten combined Optos UWF and en-face OCTA images of increasing retinal depths. The first algorithm was modified to include two scripts that automatically aligned all the en-face OCTA images. The Optos UWF image could easily be transformed to the en-face OCTA images using BigWarp with common vessel branch point landmarks in the vasculature. The resulting warped Optos image was then successfully superimposed onto the ten Optos UWF images. The scripts more easily allowed for automatic overlay of the images. Optos UWF images can be successfully superimposed onto en-face OCTA images using freely available software that has been applied to ocular use. This synthesis of multimodal imaging may increase their potential diagnostic value. Script A is publicly available at https://doi.org/10.6084/m9.figshare.16879591.v1 and Script B is available at https://doi.org/10.6084/m9.figshare.17330048.

PRESCAN Adaptive Vehicle Image Real-Time Stitching Algorithm Based on Improved SIFT

Aiming at the problems of high latency, low accuracy, and obvious stitching gaps of traditional image stitching techniques in PRESCAN, this paper proposes an adaptive real-time vehicle image stitching algorithm based on improved scale-invariant feature transform (SIFT). Firstly, a multi-threaded structure is utilized to combine the improved adaptive features from accelerated segment test algorithm with SIFT descriptors for feature extraction. Secondly, a mismatching filtering strategy based on the BF algorithm has been introduced. Finally, image stitching is performed by combining random sample consensus algorithm and adaptive region stitching strategy. The results show that the recall rate is about 30.64%, the accuracy of matching is about 98.86%, and the total stitching time is about 22.82ms. It effectively improves the quality of feature point extraction and matching accuracy and significantly reduces the stitching time of images. It provides a real-time and accurate vehicle image stitching method for the field of perception in the autonomous driving laboratory environment.