Image Points Research Articles

A high-order no image point sharp interface immersed boundary method for compressible flows

A high-order no-image point sharp interface immersed boundary method for compressible flow is presented. The method comprises a stable high-order compact scheme and a ghost point value determination method. By regulating dissipation, the stability of the compact scheme for either Dirichlet or Neumann boundary conditions is validated by the von Neumann method in one dimension. With regard to the use of ghost points, mirror points or Lagrange points are no longer employed. The boundary conditions at the intersection of arbitrary geometries and Cartesian grids are imposed on the basis function of Taylor polynomial interpolation, along with weighted least squares error minimization, in order to determine the values of the ghost points. Third-order accuracy is maintained for both subsonic and supersonic inviscid flow. Numerical simulations of several two-dimensional benchmark problems are carried out to provide evidence about the convergence order of the method.

Artificial intelligence in ophthalmology.

To review role of artificial intelligence in medicine. Artificial intelligence is continuing to revolutionize access, diagnosis, personalization of medicine, and treatment in healthcare. As a matter of fact, artificial intelligence contributed to the research that resulted in 2024 Nobel Prizes in physics, chemistry, and economics. We are only at the tip of the iceberg in utilizing the abilities of artificial intelligence in medicine to improve accuracy of diagnoses and to enhance patient outcomes. Artificial intelligence has allowed better image analysis, prediction of progression of disease, personalized treatment plans, incorporations of genomics, and improved efficiency in care and follow-up utilizing home monitoring. In ocular health diagnosis and treatment of diabetic retinopathy, macular degeneration, glaucoma, corneal infections, and ectasia are only a few examples of how the power of artificial intelligence has been harnessed. Even though there are still challenges that need more work in the areas of patient privacy, Health Insurance Portability and Accountability Act (HIPAA) compliance, reliability, and development of regulatory frameworks, artificial intelligence has revolutionized and will continue to revolutionize medicine. Artificial intelligence is enhancing medical diagnosis and treatment, as well as access and prevention. Ocular imaging, visual outcome, optics, intraocular pressure, and data points will continue to see growth it the field of artificial intelligence.

A Similar Feature Point Matching Method for Aerial Electric Power Tower Images Based on a One-Ring Neighborhood of Vertices

Due to the susceptibility of images to various factors such as scale changes, imaging conditions, and image noise, traditional feature point matching methods are difficult to achieve ideal matching accuracy, leading to many challenges in the automatic analysis and processing of power tower images. To improve the matching accuracy of similar feature points in aerial images of electric power tower, this study proposes a method for matching similar feature points in aerial images of electric power tower based on a vertex ring neighborhood, addressing the problem of large matching errors caused by factors such as scale and imaging conditions. This method first adopts the feature point extraction technique of electric power tower image based on decomposition and filtering, and constructs the Laplacian pyramid of the original image. Subsequently, filter banks are used to decompose the pyramid image in different directions, extract local extremum points as candidate feature point sets, and merge them to obtain the final feature point set. On this basis, taking into account the spatial relationships and geometric characteristics around the feature points, a texture mapping algorithm based on vertex ring neighborhood and patch classification is applied to construct a vertex ring neighborhood structure and extract geometrically representative electric power tower features. Finally, by using a texture feature point matching method based on the principle of similarity, the similarity between the real-time image and the reference image features is calculated for matching, and combined with the Babbitt coefficient to remove mismatched feature points, accurate matching of similar feature points in aerial electric power tower images is achieved. The experimental results show that this method has a mean square error of less than 0.1Pixel in matching similar texture feature points of aerial power tower images under various working conditions, significantly improving the matching accuracy and providing an effective tool for automatic analysis and processing of power tower images.

Illumination diversity in multiwavelength extreme ultraviolet ptychography

With the development of high harmonic generation (HHG), lensless extreme-ultraviolet (XUV) imaging at nanoscale resolution has become possible with table-top systems. Specifically, ptychographic phase retrieval using monochromatic XUV illumination exhibits extraordinary robustness and accuracy to computationally reconstruct the object and the illumination beam profile. In ptychography, using structured illumination has been shown to improve reconstruction robustness and image resolution by enhancing high spatial-frequency diffraction. However, broadband imaging has remained challenging, as the required multiwavelength algorithms become increasingly demanding. One major aspect is the ability to separate the available information into different physically meaningful states, such as different spectral components. Here, we show that introducing spatial diversity between spectral components of an HHG beam can significantly improve the reconstruction quality in multiwavelength XUV ptychography. We quantify the diversity in the polychromatic illumination by analyzing the diffraction patterns using established geometry- and information-theory-based dissimilarity metrics. We experimentally verify the major influence of diversity by comparing ptychography measurements using HHG beams with Gaussian and binary structured profiles as well as with beams carrying wavelength-dependent orbital angular momentum. Our results demonstrate how structured illumination acts in twofold by separating the spectral information in a single diffraction pattern while providing maximized added information with every new scan position. We anticipate our work to be a starting point for high-fidelity polychromatic imaging of next-generation nanostructured devices at XUV and soft-X-ray wavelengths.

Point cloud segmentation method based on an image mask and its application verification

Abstract Accurately perceiving three-dimensional (3D) environments or objects is crucial for the advancement of artificial intelligence interaction technologies. Currently, various types of sensors are employed to obtain point cloud data for 3D object detection or segmentation tasks. While this multi-sensor approach provides more precise 3D data than monocular or stereo cameras, it is also more expensive. The advent of RGB-D cameras, which provide both RGB images and depth information, addresses this issue. In this study, we propose a point cloud segmentation method based on image masks. By using an RGB-D camera to capture color and depth images, we generate image masks through object recognition and segmentation. Given the mapping relationship between RGB image pixels and point clouds, these image masks can be further used to extract the point cloud data of the target objects. The experimental results revealed that the average accuracy of target segmentation was 84.78%, which was close to that of PointNet++. Compared with three traditional segmentation algorithms, the accuracy was improved by nearly 23.97%. The running time of our algorithm is reduced by 95.76% compared to the PointNet++ algorithm, which has the longest running time; and by 15.65% compared to the LCCP algorithm, which has the shortest running time among traditional methods. Compared with PointNet++, the segmentation accuracy was improved. This method addressed the issues of low robustness and excessive reliance on manual feature extraction in traditional point cloud segmentation methods, providing valuable support and reference for the accurate segmentation of 3D point clouds.

Publisher Correction: Intelligent Extraction of Surface Cracks on LNG Outer Tanks Based on Close-Range Image Point Clouds and Infrared Imagery

Publisher Correction: Intelligent Extraction of Surface Cracks on LNG Outer Tanks Based on Close-Range Image Point Clouds and Infrared Imagery

Ultrasound and magnetic resonance imaging (MRI) features of angioleiomyoma.

Vascular leiomyoma or angioleiomyoma is a rare benign tumor which belongs to the group of benign smooth muscle tumors. They are commonly located at the extremities, usually presented as a painful solitary lesion in the subcutaneous tissue. To date, very few reports describe its characteristics from an imaging point of view, especially their ultrasound characteristics, making it difficult to do a pre-surgical diagnosis. Our purpose is to describe the clinical, pathologic and imaging features of angioleiomyoma [ultrasound, Doppler ultrasound and magnetic resonance imaging (MRI) findings]. We retrospectively reviewed from the pathology database from Hospital Universitario Fundación Alcorcón, Madrid, Spain, the clinical histories of 139 patients who had surgical excision and histologic diagnosis of angioleiomyoma during the last 17 years, from May 31st 2006 to April 17th 2023. Of those patients, we focused on 50 who had soft tissue angioleiomyoma with imaging study [ultrasonography (US) and/or MRI] performed in our institution, making a descriptive cross-sectional study. In our series, a very characteristic ultrasonographic and US Doppler pattern was found. It consists in the presence of a well defined, homogeneous and highly vascularized soft tissue tumor, sometimes with the presence of a feeding vessel. When the described US features appear in a mobile and elastic subcutaneous slow growing tumor on an extremity, the diagnosis of angioleiomyoma should be considered as highly probable.

High-order discontinuous Galerkin method with immersed boundary treatment for compressible flows on parallel adaptive Cartesian grids

Adaptive mesh refinement (AMR) technology and high-order methods are important means to improve the quality of simulation results and have been hotspots in the computational fluid dynamics community. In this paper, high-order discontinuous Galerkin (DG) and direct DG (DDG) finite element methods are developed based on a parallel adaptive Cartesian grid to simulate compressible flow. On the one hand, a high-order multi-resolution weighted essentially nonoscillatory limiter is proposed for DG and DDG methods. This limiter can enhance the stability of DG/DDG methods for compressible flows dominated by shock waves. It is also compact, making it suitable for the implementation of AMR with frequent refinement/coarsening. On the other hand, a coupling method of DG and immersed boundary method is proposed to simulate flow around objects. Due to the compactness of DG, the physical quantities of image points can be directly obtained through the DG/DDG polynomial of the corresponding cells. It avoids the wide interpolation stencil of traditional IBM and makes it more suitable for the parallel adaptive Cartesian grid framework in this paper. Finally, the performance of the proposed method is verified through typical two- and three-dimensional cases. The results indicate that the method proposed in this paper has low numerical dissipation in smooth areas and can effectively handle compressible flow dominated by discontinuities. Moreover, for transonic flow over a sphere, the error of results between the proposed method and direct numerical simulation is within 1%, fully validating the accuracy of the method presented in this paper.

An Enhanced Multiscale Retinex, Oriented FAST and Rotated BRIEF (ORB), and Scale-Invariant Feature Transform (SIFT) Pipeline for Robust Key Point Matching in 3D Monitoring of Power Transmission Line Icing with Binocular Vision

Power transmission line icing (PTLI) poses significant threats to the reliability and safety of electrical power systems, particularly in cold regions. Accumulation of ice on power lines can lead to severe consequences, such as line breaks, tower collapses, and widespread power outages, resulting in economic losses and infrastructure damage. This study proposes an enhanced image processing pipeline to accurately detect and match key points in PTLI images for 3D monitoring of ice thickness using binocular vision. The pipeline integrates established techniques such as multiscale retinex (MSR), oriented FAST and rotated BRIEF (ORB) and scale-invariant feature transform (SIFT) algorithms, further refined with m-estimator sample consensus (MAGSAC)-based random sampling consensus (RANSAC) optimization. The image processing steps include automatic cropping, image enhancement, feature detection, and robust key point matching, all designed to operate in challenging environments with poor lighting and noise. Experiments demonstrate that the proposed method significantly improves key point matching accuracy and computational efficiency, reducing processing time to make it suitable for real-time applications. The effectiveness of the pipeline is validated through 3D ice thickness measurements, with results showing high precision and low error rates, making it a valuable tool for monitoring power transmission lines in harsh conditions.

Point of care ultrasound measurement of paralumbar caudal vena cava diameter and caudal vena cava to aortic ratio in hypovolemic dogs.

Accurate assessment of intravascular volume is critical for precise fluid prescription. In people, bedside or point of care ultrasound is used to measure the inferior vena cava, with or without paired aortic measurement, to estimate intravascular volume. To determine if point of care ultrasound measurement of the caudal vena cava (CVC) diameter or the CVC diameter to the abdominal aorta (Ao) diameter (CVC:Ao) at the paralumbar view are associated with changes in intravascular volume, mean arterial pressure (MAP), or cardiac output in normovolemic and hypovolemic dogs. 8 purpose-bred dogs. Pressure-targeted hemorrhagic shock was induced in purpose-bred dogs under general anesthesia. Dogs were exsanguinated to a mean arterial pressure of 40 mmHg, or a maximum 60% blood volume lost, then auto-transfused shed blood. At a left paralumbar view, longitudinal plane measurements of the abdominal CVC diameter and aortic diameter were obtained. Measurements were performed at 4 timepoints: baseline under anesthesia (TP1), after hemorrhagic shock was induced (TP2), after ½ of shed blood had been re-transfused (TP3), and post-resuscitation with completed re-transfusion (TP4). Additional variables collected included cardiac output using thermodilution and arterial blood pressure. CVC:Ao was not significantly different between timepoints and was not associated with changes in CO (p = 0.28) or MAP (p = 0.50). CVC diameter was significantly different between baseline (TP1) and hemorrhagic shock (TP2). CVC diameter was significantly different at TP2 compared to TP1 after controlling for the effect of CO (p = 0.03) and MAP (p = 0.001). Aortic diameter was also significantly different at TP2 (p = 0.002, p = 0.001) and TP3 (p = 0.023, p = 0.017) compared to TP1 after controlling for CO and MAP. Obtaining point of care ultrasound images for CVC:Ao measurement was feasible. With a marked decrease in intravascular volume, both CVC and Ao diameter decreased, resulting in an unchanged CVC:Ao. Despite changes in CVC and Ao diameters, these changes were not associated with measured changes in CO, emphasizing that CO is not a direct estimate of intravascular volume and is affected by many compensatory mechanisms. Additional studies are needed to determine the most accurate method for bedside measurement of intravascular volume status.

Robust and accurate visual geo-localization using prior map constructed by handheld LiDAR SLAM with camera image and terrestrial LiDAR point cloud

ABSTRACT Visual geo-localization using prior map of known environments has extensive applications in the fields such as self-driving, augmented reality and navigation. Currently, such prior maps are usually constructed by visual SLAM or SFM. However, the recent advances in LiDAR SLAM technology with RGB camera demonstrate great potential in efficient and accurate prior map construction and visual geo-localization. In this research, we developed a novel prior map construction approach which seamlessly integrates a handheld LiDAR SLAM system, a global LiDAR localization algorithm and a sparse set of terrestrial LiDAR scans as accurate control within a factor graph optimization framework. The developed LiDAR SLAM system augmented by terrestrial LiDAR scans achieves an improved mapping accuracy of 8 cm under GNSS-denied conditions in the areas over 50,000 m2 while the state-of-art FAST-LIO2 alone fails and produces a mapping error over 30 m. The results show that a competitive accuracy of visual geo-localization on a mobile phone using the constructed prior map is achieved (i.e. 60 and 76 cm in two tests respectively). Finally, this work also demonstrates a novel LiDAR point cloud fusion method that produces optimized and consistent coarse-to-fine 3D reconstruction in large and complex scenes.

A Novel Fuzzy Image-Based UAV Landing Using RGBD Data and Visual SLAM

In this work, an innovative perception-guided approach is proposed for landing zone detection and realization of Unmanned Aerial Vehicles (UAVs) operating in unstructured environments ridden with obstacles. To accommodate secure landing, two well-established tools, namely fuzzy systems and visual Simultaneous Localization and Mapping (vSLAM), are implemented into the landing pipeline. Firstly, colored images and point clouds acquired by a visual sensory device are processed to serve as characterizing maps that acquire information about flatness, steepness, inclination, and depth variation. By leveraging these images, a novel fuzzy map infers the areas for risk-free landing on which the UAV can safely land. Subsequently, the vSLAM system is employed to estimate the platform’s pose and an additional set of point clouds. The vSLAM point clouds presented in the corresponding keyframe are projected back onto the image plane on which a threshold fuzzy landing score map is applied. In other words, this binary image serves as a mask for the re-projected vSLAM world points to identify the best subset for landing. Once these image points are identified, their corresponding world points are located, and among them, the center of the cluster with the largest area is chosen as the point to land. Depending on the UAV’s size, four synthesis points are added to the vSLAM point cloud to execute the image-based visual servoing landing using image moment features. The effectiveness of the landing package is assessed through the ROS Gazebo simulation environment, where comparisons are made with a state-of-the-art landing site detection method.

Visible light infrared matching algorithm based on improved SuperPoint and linear converter

In order to solve the problems of high difficulty and mismatch rate in heterogeneous image matching of visible light and infrared images, a deep learning matching algorithm based on improved SuperPoint and linear transformer is proposed. The algorithm first introduces the idea of feature pyramid to construct a feature description branch based on the SuperPoint network structure, and trains it based on the hinge loss function, so as to better learn the multi-scale deep features of visible light and infrared images and increase the similarity of image point pair descriptors with the same name; in the feature matching module, the linear transformer is used to improve the SuperGlue matching algorithm and aggregate features to improve matching performance. The proposed algorithm is experimentally verified on multiple data sets. The results show that compared with the existing algorithms, the algorithm achieves better matching results and improves the matching accuracy.

UPT-Flow: Multi-scale transformer-guided normalizing flow for low-light image enhancement

Low-light images often suffer from information loss and RGB value degradation due to extremely low or nonuniform lighting conditions. Many existing methods primarily focus on optimizing the appearance distance between the enhanced image and the normal-light image, while neglecting the explicit modeling of information loss regions or incorrect information points in low-light images. To address this, this paper proposes an Unbalanced Points-guided multi-scale Transformer-based conditional normalizing Flow (UPT-Flow) for low-light image enhancement. We design an unbalanced point map prior based on the differences in the proportion of RGB values for each pixel in the image, which is used to modify traditional self-attention and mitigate the negative effects of areas with information distortion in the attention calculation. The Multi-Scale Transformer (MSFormer) is composed of several global-local transformer blocks, which encode rich global contextual information and local fine-grained details for conditional normalizing flow. In the invertible network of flow, we design cross-coupling conditional affine layers based on channel and spatial attention, enhancing the expressive power of a single flow step. Without bells and whistles, extensive experiments on low-light image enhancement, night traffic monitoring enhancement, low-light object detection, and nighttime image segmentation have demonstrated that our proposed method achieves state-of-the-art performance across a variety of real-world scenes. The code and pre-trained models will be available at https://github.com/NJUPT-IPR-XuLintao/UPT-Flow.

Dense projection fusion for 3D object detection

Fusing information from LiDAR and cameras can effectively enhance the overall perceptivity of autonomous vehicles in various scenarios. Despite the relatively good results achieved by point-wise fusion and Bird’s-Eye-View (BEV) fusion, they still cannot fully leverage the image information and lack of effective depth information. For any fusion methods, the multi-modal features first need to be concatenated along the channel, and then the fused features are extracted using convolutional layers. However, this type of fusion methods is effective, but too coarse which causes that the fused features cannot pay more attention to the regions with important features and suffer from severe noise. To tackle these issues, we propose in this paper a Dense Projection Fusion (DPFusion) approach. It consists of two new modules: dense depth map guided BEV transform (DGBT) module and multi-modal feature adaptive fusion (MFAF) module. The DGBT module first quickly estimates the depth of each pixel and then projects all image features to the BEV space, making full use of the image information. The MFAF module computes the image weights and point cloud weights for each channel in each BEV grid and then adaptively weights and fuses the image BEV features with the point cloud BEV features. It is worth pointing out that the MFAF module makes the fused features pay more attention to background outline and object outline. Our proposed DPFusion demonstrates competitive results in 3D object detection, achieving a mean Average Precision (mAP) of 70.4 and a nuScenes detection score (NDS) of 72.3 on the nuScenes validation set.

Analysis of the Grid Quantization for the Microwave Radar Coincidence Imaging Based on Basic Correlation Algorithm

In Microwave Radar Coincidence Imaging (MRCI), the imaging region is typically discretized into a fine grid. In other words, it assumes that the equivalent scatterers of the target are precisely located at the centers of these pre-discretized grids. However, this approach usually encounters the off-grid problem, which can significantly degrade the imaging performance. In this paper, to establish a criterion for grid quantization, the performance of the MRCI system related to the grid size and the distribution of imaging points is investigated. First, the discretization of the imaging scene is regarded as a random sampling problem, and the off-grid imaging model for MRCI is established. Then, the probability distribution function (PDF) of the imaging amplitude for a single point target is analyzed, and the mean first-order imaging error (MFE) for multiple point targets is derived based on the Basic Correlation Algorithm (BCA). Finally, the relationship between the grid quantization of the imaging area and the performance of the MRCI system is analyzed, providing a theoretical guidance for enhancing the performance of MRCI. The validity of the analyses is verified through simulation experiments.

Surface insight: Leveraging high-density dataset fusion for enhanced roughness classification

The ability to assess the surface quality quickly and accurately is of immense importance in manufacturing system. Modern metrology system along with machine learning is great at classification but requires more time. Traditionally accessing surface roughness is a time-consuming process. The progress in manufacturing technology necessitates improved approaches for quality control, specifically in the categorization of surface roughness, which has a substantial impact on the performance of materials. This research study introduces a novel method for classifying surface roughness by combining image data and point cloud data to create a comprehensive model. It then compares the performance of this model with a model that just relies on image data. A comprehensive analysis is conducted in this study, where image and point cloud data is collected and analysed. Multilinear principal component analysis (MPCA) along with random forest classifier is employed to create a model that classifies the surface texture. The primary goal is to showcase the enhanced precision and comprehensive understanding offered by the fused data model compared to the model that solely relies on images.Furthermore, the work presents a pragmatic approach for developing this enhanced model offline and applying it online in real-time production environments, with a particular focus on using only image data. This strategy is in line with the objectives of Industry 4.0, which seeks to achieve more intelligent and data-driven manufacturing processes. Subsequent investigations will prioritize expanding the model’s suitability to various manufacturing settings, particularly highlighting its capacity to ensure quality in manufacturing lines through the utilization of images.

Computing laminate composite voxel properties using Mirtich formulas

AbstractComputational homogenization methods offer deeper insights into the mechanical behavior of heterogeneous materials, often exhibiting complex anisotropic properties due to intricate manufacturing processes. Micromechanical techniques exploit the knowledge of the material's microstructure to predict effective behavior. Digital material data, represented as voxels, require processing numerous image points. Despite the computational power of modern fast Fourier transform‐based numerical solvers, performance improvements are still valuable. The composite voxel technique, in its original form, furnishes voxels containing more than one material with a surrogate material law, resulting in higher accuracy. This work contributes to the understanding and application of composite voxels in computational micromechanics by integrating them into a level‐set‐based framework, providing a detailed methodology for deriving laminate composite properties using the formulas of Mirtich, and demonstrating their potential to enhance computational performance through a numerical example.

Overview of Structure from Motion

In this paper is given an overview of tools and techniques for obtain information about the geometry of 3D scenes from 2D images. The generating three-dimensional structure from a series of 2D images or video of scenes is known as Structure from Motion (SfM). A central tenet of structure from motion is that, given the position of a feature in one image, it is possible to find the corresponding position of the same feature in successive images. We described methods for the simultaneous recovery of 3D points and camera projection matrices using corresponding image points in multiple views. Structure from Motion (SfM) is a fascinating field within computer vision that seeks to reconstruct a three-dimensional structure of the environment from a sequence of two-dimensional images. At the heart of SfM lies a set of sophisticated algorithms that enable the extraction of spatial information from a series of images. Two public repositories that may be of use are Open SfM and Colmap.

Enhanced Estimation of Crown-Level Leaf Dry Biomass of Ginkgo Saplings Based on Multi-Height UAV Imagery and Digital Aerial Photogrammetry Point Cloud Data

Ginkgo is a multi-purpose economic tree species that plays a significant role in human production and daily life. The dry biomass of leaves serves as an accurate key indicator of the growth status of Ginkgo saplings and represents a direct source of economic yield. Given the characteristics of flexibility and high operational efficiency, affordable unmanned aerial vehicles (UAVs) have been utilized for estimating aboveground biomass in plantations, but not specifically for estimating leaf biomass at the individual sapling level. Furthermore, previous studies have primarily focused on image metrics while neglecting the potential of digital aerial photogrammetry (DAP) point cloud metrics. This study aims to investigate the estimation of crown-level leaf biomass in 3-year-old Ginkgo saplings subjected to different nitrogen treatments, using a synergistic approach that combines both image metrics and DAP metrics derived from UAV RGB images captured at varying flight heights (30 m, 60 m, and 90 m). In this study, image metrics (including the color and texture feature parameters) and DAP point cloud metrics (encompassing crown-level structural parameters, height-related and density-related metrics) were extracted and evaluated for modeling leaf biomass. The results indicated that models that utilized both image metrics and point cloud metrics generally outperformed those relying solely on image metrics. Notably, the combination of image metrics obtained from the 60 m flight height with DAP metrics derived from the 30 m height significantly enhanced the overall modeling performance, especially when optimal metrics were selected through a backward elimination approach. Among the regression methods employed, Gaussian process regression (GPR) models exhibited superior performance (CV-R2 = 0.79, rRMSE = 25.22% for the best model), compared to Partial Least Squares Regression (PLSR) models. The common critical image metrics for both GPR and PLSR models were found to be related to chlorophyll (including G, B, and their normalized indices such as NGI and NBI), while key common structural parameters from the DAP metrics included height-related and crown-related features (specifically, tree height and crown width). This approach of integrating optimal image metrics with DAP metrics derived from multi-height UAV imagery shows great promise for estimating crown-level leaf biomass in Ginkgo saplings and potentially other tree crops.