Matching Points Research Articles

Local feature matching and tracking optimization of machine VSLAM based on AG Sinkhorn

SummaryAt present, the visual Simultaneous localization and mapping method has a wide range of applications in mobile robots and unmanned driving. However, when the environment changes, the accuracy of the system drops sharply. Aiming at the low matching accuracy of local feature matching and tracking optimization in current visual simultaneous localization and mapping methods, the attention map neural network and Sinkhorn algorithm are used to extract and match the image features. Then, it improves the real‐time performance of the visual simultaneous localization and mapping system by local optimization of feature point matching, and proposes an optimization method for visual simultaneous localization and mapping in view of an end‐to‐end algorithm. The experiment illustrates that in the experimental comparison of HPatches View dataset, the Homography estimation was improved by 0.092, the matching accuracy was improved by 0.087, and the matching recall was improved by 0.038. In the experimental comparison of HPatches Illum dataset, the Homography estimation was improved by 0.015, the matching accuracy is improved by 0.076, and the matching recall was improved by 0.036. The local feature matching and tracking optimization method of the visual Simultaneous localization and mapping system through the attention map neural network and Sinkhorn algorithm can enhance the matching accuracy of local feature matching and tracking optimization in dynamic environment and under changing lighting conditions.

The Psychological Construction of Early Childhood Art Education

The psychological construction of early childhood art education refers to the process of promoting children's mental health, emotional development, and personality shaping through art education activities. Art Education provides a platform for early childhood to express themselves, achieve self-realization, unleash their wisdom, develop own aesthetic standards, learn cooperate and communication skills, achieve psychological balance and establish understanding and respect. This study highlights the strategic shift in the approach to art education in China, with a focus on interdisciplinary education and the integration of art into extracurricular activities. With the issue on knowledge is often imparted in a one-way manner and children is encouraged to passively imitate, does not align with the developmental needs of young children or the goals of comprehensive education, this paper researches the dynamic process that involves teachers, students, and sometimes parents and community members in a cycle of planning, acting, observing, and reflecting to improve educational practices, and outlines the Mission and Vision on Development goals and the level of emphasis placed on families and kindergartens, Professional advantages of promoting mental hygiene through art education and Matching of key points, principles, and strategies.

An adaptive grouping sonar-inertial odometry for underwater navigation

This paper presents a sonar-inertial navigation system with an adaptive grouping optimization framework, which tightly couples inertial measurements with acoustic data from forward-looking sonar (FLS). The system’s front-end uses the sonar-inertial constraint-based feature matching (SICFM) to increase the accuracy and robustness of data association. SICFM is designed by incorporating two novel measures. Firstly, it combines prior motion information to refine the feature search region. Then, a Gaussian-gamma mixture model is formulated to reduce the impact of speckle noise on the selection of matching points. In the back-end, an adaptive grouping factor graph optimization framework is established. A frame manager is designed using the solvability of motion estimation to dynamically group sonar frames, thereby ensuring stable feature tracking for factor graph optimization. Pre-integration-based inertial measurement factor and reprojection-based sonar measurement factor are constructed as constraints in the optimization, which reduces the dependence on prior knowledge of the scene. Furthermore, an elevation angle parameterization is introduced, aiming at the nonlinear projection of sonar features. The evaluation is presented in simulation and real-world experiments, which validate our proposed algorithm in terms of robust localization with high accuracy.

Local topology similarity guided probabilistic sampling for mismatch removal

Feature point matching between two images is a fundamental and important process in machine vision. In many cases, mismatches are inevitable, and removing mismatches is an indispensable task. The existing methods attempt to find comprehensive constraints or sampling model to achieve better performance, which results in the increasingly complexity and may cause the weakness of the generality and scalability. To address this issue, a method called Local Topology similarity guided probabilistic Sampling consensus (LTS) is proposed. It constructs a topological network, then quantifies the mismatch probability in a concise approach based on comparing the topological relationship with neighbourhoods. Then, it detects and removes the mismatches by sampling guided by the mismatch probability. Compared with the state-of-the-art methods, LTS has an excellent performance in accuracy and robustness.

CGAN-Based Forest Scene 3D Reconstruction from a Single Image

Forest scene 3D reconstruction serves as the fundamental basis for crucial applications such as forest resource inventory, forestry 3D visualization, and the perceptual capabilities of intelligent forestry robots in operational environments. However, traditional 3D reconstruction methods like LiDAR present challenges primarily because of their lack of portability. Additionally, they encounter complexities related to feature point extraction and matching within multi-view stereo vision sensors. In this research, we propose a new method that not only reconstructs the forest environment but also performs a more detailed tree reconstruction in the scene using conditional generative adversarial networks (CGANs) based on a single RGB image. Firstly, we introduced a depth estimation network based on a CGAN. This network aims to reconstruct forest scenes from images and has demonstrated remarkable performance in accurately reconstructing intricate outdoor environments. Subsequently, we designed a new tree silhouette depth map to represent the tree’s shape as derived from the tree prediction network. This network aims to accomplish a detailed 3D reconstruction of individual trees masked by instance segmentation. Our approach underwent validation using the Cityscapes and Make3D outdoor datasets and exhibited exceptional performance compared with state-of-the-art methods, such as GCNDepth. It achieved a relative error as low as 8% (with an absolute error of 1.76 cm) in estimating diameter at breast height (DBH). Remarkably, our method outperforms existing approaches for single-image reconstruction. It stands as a cost-effective and user-friendly alternative to conventional forest survey methods like LiDAR and SFM techniques. The significance of our method lies in its contribution to technical support, enabling the efficient and detailed utilization of 3D forest scene reconstruction for various applications.

QUIZ: An arbitrary volumetric point matching method for medical image registration

Rigid pre-registration involving local–global matching or other large deformation scenarios is crucial. Current popular methods rely on unsupervised learning based on grayscale similarity, but under circumstances where different poses lead to varying tissue structures, or where image quality is poor, these methods tend to exhibit instability and inaccuracies. In this study, we propose a novel method for medical image registration based on arbitrary voxel point of interest matching, called query point quizzer (QUIZ). QUIZ focuses on the correspondence between local–global matching points, specifically employing CNN for feature extraction and utilizing the Transformer architecture for global point matching queries, followed by applying average displacement for local image rigid transformation.We have validated this approach on a large deformation dataset of cervical cancer patients, with results indicating substantially smaller deviations compared to state-of-the-art methods. Remarkably, even for cross-modality subjects, it achieves results surpassing the current state-of-the-art.

Point cloud based hand gesture recognition using template matching

Hand gesture recognition is important in human-computer interaction with wide applications in many fields. Different from common hand gesture recognition based on 2D images acquired from RGB camera, the utilization of 3D images provides additional spatial information about the target and attracts more and more attention in hand gesture recognition. However, most 3D images for hand gesture recognition are based on depth maps, which only take the distance information as a channel of 2D images, without taking full use of the 3D information. Besides, greater data volume of 3D images brings challenges to the arithmetic facility of hand gesture recognition. Here, we proposed a point cloud based method for hand gesture recognition. To fully use the 3D information, plane points for template matching were extracted based on their normal distributions, which leads to the average recognition rate over 97%. Pre-classification was implemented to ensure a high-efficient recognition without additional requirements for the computer. The proposed method may provide approach for accurate and efficient hand gesture recognition based on 3D images.

Research on Image Stitching Algorithm Based on Point-Line Consistency and Local Edge Feature Constraints.

Image stitching aims to synthesize a wider and more informative whole image, which has been widely used in various fields. This study focuses on improving the accuracy of image mosaic and proposes an image mosaic method based on local edge contour matching constraints. Because the accuracy and quantity of feature matching have a direct influence on the stitching result, it often leads to wrong image warpage model estimation when feature points are difficult to detect and match errors are easy to occur. To address this issue, the geometric invariance is used to expand the number of feature matching points, thus enriching the matching information. Based on Canny edge detection, significant local edge contour features are constructed through operations such as structure separation and edge contour merging to improve the image registration effect. The method also introduces the spatial variation warping method to ensure the local alignment of the overlapping area, maintains the line structure in the image without bending by the constraints of short and long lines, and eliminates the distortion of the non-overlapping area by the global line-guided warping method. The method proposed in this paper is compared with other research through experimental comparisons on multiple datasets, and excellent stitching results are obtained.

Blowout Fluid Velocity Measurement Method based on High-speed Sequence Images

Given the high-temperature and high-pressure environment after the blowout, the staff usually cannot calculate and analyze various blowout parameters at close range and thus take effective measures to control accidents quickly. In this paper, a blowout fluid velocity measurement method is proposed based on high-speed sequence images, and simulation experiments are conducted. The scene conditions of blowout accidents are simulated and analyzed. On this basis, cameras are adopted to shoot a large number of simulated blowout fluid videos from different angles under outdoor conditions. Afterward, the video is converted into a multi-frame image, and the blowout fluid image feature point detection and extraction model are established based on the orb (oriented fast and rotated brief) algorithm. On the feature points extracted by the model, the gms algorithm is used to match the features of the blowout fluid images in the front and back frames. Additionally, the wrong matching points are eliminated using the ransac (random sample consensus) algorithm, and the obtained feature point pairs are employed into the flow velocity calculation model for flow velocity calculation. The results demonstrate that the measured fluid velocity is more accurate. The method proposed in this study can provide reliable data support for early blowout emergency rescue.

Image moment extraction based aerial photo selection for UAV high-precision geolocation without GPS

Since GPS is susceptible to environmental interference or cannot be used in some specific situations, it is important to study the UAV geolocation under GPS-free condition. How to ensure that the aerial photos used for positioning have high accuracy is a pressing concern. The conventional approach relies on the number of matching feature points between aerial and satellite photos and verifying whether the aerial photo center projection falls within a designated area of the satellite photo. However, this method often yields suboptimal accuracy. This paper introduces a novel method for selecting aerial photos for geolocation based on an image moment extraction module. The image moment extraction module contains two parts: the relative error of the image invariant moment and the area relative error. The former is used to select the aerial photos with good performance in translation, rotation and scaling before and after projection, and the latter is adopted to reject aerial photos with excessive scaling before and after projection. Through experiments, we demonstrate the effectiveness and superiority of this approach. Results show that the image moment extraction module, when used for positioning with aerial photo center projection, significantly reduces the mean absolute error in UAV geolocation from 63.220 m to 9.454 m. The maximum error is reduced from 486.7 m to 47.194 m, with 70.96% of aerial photos suitable for positioning. This research is pivotal for achieving high-precision UAV geolocation in GPS-free environments.

Appearance flow estimation for online virtual clothing warping via optimal feature linear assignment

Clothing warping to spatially align source garments with the corresponding body parts is crucial in clothing media tasks such as virtual try-on and pose-guided person generation. Recent pioneering work has utilized flow fields with additional dimensions of freedom to simulate clothing warping flexibly. However, current methods for estimating appearance flow typically rely on calculating just the local cost volume which contains multiple noisy matching points, potentially leading to a mismatch between clothing and body parts. To address this issue, we propose a novel appearance flow estimation network(Warping-Flow) for clothing warping based on optimal features linear assignment. Specially, two remarkable contributions are made to improve feature matching precision. First, a local context feature aggregation module is proposed to enhance the semantic feature distinction of the source cloth and target pose. Second, Warping-Flow estimates a hard attention mask through the cost volume to filter irrelevant features, followed by the optimal linear assignment algorithm to normalize the cost volume to a discrete permutation matrix that explicitly models the most contributing bipartite matches. Experiments conducted on the VITON and VITON-HD datasets demonstrate that Warping-Flow outperforms existing state-of-the-art algorithms, particularly in cases involving complex clothing deformation. Furthermore, Warping-Flow can serve as a plug-in to improve existing garment media technologies.

Directional region-based feature point matching algorithm based on SURF.

Feature point matching is one of the fundamental tasks in binocular vision. It directly affects the accuracy and quality of 3D reconstruction. This study proposes a directional region-based feature point matching algorithm based on the SURF algorithm to improve the accuracy of feature point matching. First, same-name points are selected as the matching reference points in the left and right images. Then, the SURF algorithm is used to extract feature points and construct the SURF feature point descriptors. During the matching process, the location relationship between the query feature point and the reference point in the left image is directed to determine the corresponding matching region in the right image. Then, the matching is completed within this region based on Euclidean distance. Finally, the grid-based motion statistics algorithm is used to eliminate mismatches. Experimental results show that the proposed algorithm can substantially improve the matching accuracy and the number of valid matched points, particularly in the presence of a large amount of noise and interference. It also exhibits good robustness and stability.

Feature matching based on Gaussian kernel convolution and minimum relative motion

Feature matching is a necessary and important step for remote sensing image registration, intended to establish reliable point correspondences between two sets of features. In this paper, we propose a feature registration model based on local relative motion, which combines Gaussian kernel convolution with relative motion (GRM) vector to obtain better results by removing wrong matches and improving the inlier point accuracy. We first establish putative matching based on the similarity between local descriptors. Then, the preliminary hypothetical matching point set is filtered using consistency with nearest neighbors among the inlier points to obtain a more accurate motion vector, and to fit the real motion vector through the Gaussian convolution kernel. Finally, we find the displacement between the fitted motion vector and the matching generated motion vector. And combine the displacement with the optimization model to find the inlier point set. Experimental results show that our GRM method outperforms related work, achieving better matching results.

Unleashing the Power of Plasma and Flow: A Novel Cold Plasma‐Oscillatory System for Enhanced Continuous Biodiesel Production from Sunflower Oil

While fossil fuel resources are limited, the increasing demand for energy in the industrial and transportation sectors has led to using renewable fuels. This study aims to improve biodiesel production from sunflower oil by combining cold plasma and oscillatory systems through a transesterification process. The four main operational variables are examined, such as molar ratio (MR) (1:6–1:12), catalyst concentration (CC) (0.5–1.25 wt%), plasma exposure time (PET) (25–75 s), and residence time (RT) (30–90 s). The smooth periodic constriction reactor's physical attributes, including the diameter of the tube (D), the diameter of the constricted region (d0), and the distance between baffles (l), as well as the properties of the cold plasma system, are determined. The data analysis presents that PET, RT, CC, and MR have the greatest effect on efficiency. This optimization indicates that by exposing the reaction mixture to plasma for 59.99 s, adding 1.09% by weight of the catalyst, and with a MR of 7.17 in 86.43 s, biodiesel with a conversion percentage of 94.75% is produced. The results show that the optimum point matches with EN 14214 standard.

Evaluation of GPM DPR Rain Parameters with North Taiwan Disdrometers

Abstract Global precipitation demonstrates a substantial role in the hydrological cycle and offers tremendous implications in hydrometeorological studies. Advanced remote sensing instrumentations, such as the NASA Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR), can estimate precipitation and cloud properties and have a unique capability to estimate the raindrop size information globally at snapshots in time. The present study validates the Level-2 data products of the GPM DPR with the long-term measurements of seven north Taiwan Joss–Waldvogel disdrometers from 2014 to 2022. The precipitation and drop size distribution parameters like rainfall rate (R; mm h−1), radar reflectivity factor (dBZ), mass-weighted mean drop diameter (Dm; mm), and normalized intercept parameter (Nw; m−3 mm−1) of the GPM DPR are compared with the disdrometers. Four different comparison approaches (point match, 5-km average, 10-km average, and optimal method) are used for the validation; among these four, the optimal strategy provided reasonable agreement between the GPM DPR and the disdrometers. The GPM DPR revealed superior performance in estimating the rain parameters in stratiform precipitation than the convective precipitation. Irrespective of algorithm type (dual- or single-frequency algorithm), sensitivity analysis revealed superior agreement for the mass-weighted mean diameter and inferior agreement for the normalized intercept parameter.

A high-precision image registration method for multi-channel single molecule localization

Single-molecule localization technology has been widely used in single-particle tracking and super-resolution imaging of biological samples, as it can bypass the diffraction limit of optical systems. Multi-channel single-molecule localization uses multiple imaging channels to simultaneously track different targets or perform multi-color super-resolution imaging, and can also improve the axial depth of single-particle tracking or achieve higher localization precision and density for super-resolution imaging. However, the difference between images in each channel can affect collaborative localization or quantitative analysis, so image registration is a key step in its image data preprocessing. Moreover, due to the high precision of single-molecule localization, its requirements for multi-channel image registration accuracy are also high. Existing technologies generally use control point-based registration methods and often use complicated and precise methods to obtain fiducial images for locating control point pairs to achieve high-precision image registration, which involves high sample or experimental equipment requirements and is difficult to directly extend to other systems. Therefore, developed in this work, is a high-precision image registration method that can directly use randomly distributed fluorescent beads as fiducial samples based on local nonlinear transformation and elimination of mismatched points. By monitoring and iteratively filtering control points in the process of feature matching and transformation model parameter estimation to eliminate control point pairs that are not accurately matched due to inaccurate or poor precision of single-molecule localization, the adverse effects on accurate acquisition and precise matching of control points when using randomly distributed fluorescent beads as fiducial samples are eliminated. At the same time, a second-order polynomial fitting based on local weighted mean is used for estimating the transformation model parameter to better adapt to the existence of local nonlinear deformation between different channels. The results show that using this method only requires three iterations to find and eliminate control point pairs that are not accurately located and matched, thereby achieving more accurate transformation model parameter and improving the registration accuracy by an order of magnitude, achieving a registration accuracy of about 6 nm in a complex dual-channel single-molecule localization imaging system based on orthogonal astigmatism.

Optimization of Binocular Vision Ranging Based on Sparse Stereo Matching and Feature Point Extraction

Optimization of Binocular Vision Ranging Based on Sparse Stereo Matching and Feature Point Extraction

Design of a High Speed 360-degree Panoramic Video Acquisition System Based on FPGA and USB 3.0

This paper presents a FPGA-based hardware architecture for capturing real-time panoramic images and the parallel implementation of an image stitching algorithm based on the scale-invariant feature transform (SIFT) features. First, the system transfers the images captured by five CMOS sensors to the designed FPGA board through the FMC adapter plate. Then, the system employs the FPGA-based parallel computing to extract the SIFT features and detect the best matching region and points for image registration. Finally, in order to obtain the panoramic image with smooth changes at the junctions, images of the adjacent images are fused. Using the designed FPGA platform makes it possible to extract the SIFT and stitch the images in real-time, which is not realized in most panoramic imaging systems. The panoramic system, developed using the Xilinx Zynq®-7000 all programmable SOC, can capture video of 5*640*480 images at the speed of 60 fps. To enable high-speed image transfer from the imaging system to the host computer, the USB3.0 interface with the transmission speed of 359MB/s has been developed. The real-time efficiency of the parallel system will be demonstrated by the simulations and experiments.

ANALYSIS OF IMAGE KEY POINT DETECTION, DESCRIPTION AND MATCHING METHODS

ANALYSIS OF IMAGE KEY POINT DETECTION, DESCRIPTION AND MATCHING METHODS

Point cloud matching algorithm based on adaptive local neighborhood conditions

Point cloud matching algorithm based on adaptive local neighborhood conditions