Search Results (20)

Feature matching is crucial in image recognition. However, blurring caused by illumination changes often leads to deviations in local appearance-based similarity, resulting in ambiguous or false matches—an enduring challenge in computer vision. To address this issue, this paper proposes a method named MultS-ORB (Multistage Oriented FAST and Rotated BRIEF). The proposed method preserves all the advantages of the traditional ORB algorithm while significantly improving feature matching accuracy under illumination-induced blurring. Specifically, it first generates initial feature matching pairs using KNN (K-Nearest Neighbors) based on descriptor similarity in the Hamming space. Then, by introducing a local motion smoothness constraint, GMS (Grid-Based Motion Statistics) is applied to filter and optimize the matches, effectively reducing the interference caused by blurring. Afterward, the PROSAC (Progressive Sampling Consensus) algorithm is employed to further eliminate false correspondences resulting from illumination changes. This multistage strategy yields more accurate and reliable feature matches. Experimental results demonstrate that for blurred images affected by illumination changes, the proposed method improves matching accuracy by an average of 75%, reduces average error by 33.06%, and decreases RMSE (Root Mean Square Error) by 35.86% compared to the traditional ORB algorithm. Full article

This research introduces an innovative probabilistic method for examining torsional stress behavior in spherical shell structures through Monte Carlo simulation techniques. The spherical geometry of these components creates distinctive computational difficulties for conventional analytical and deterministic numerical approaches when solving torsion-related problems. The authors develop a comprehensive mesh-free Monte Carlo framework built upon the Feynman–Kac formula, which maintains the geometric symmetry of the domain while offering a probabilistic solution representation via stochastic processes on spherical surfaces. The technique models Brownian motion paths on spherical surfaces using the Euler–Maruyama numerical scheme, converting the Saint-Venant torsion equation into a problem of stochastic integration. The computational implementation utilizes the Fibonacci sphere technique for achieving uniform point placement, employs adaptive time-stepping strategies to address pole singularities, and incorporates efficient algorithms for boundary identification. This symmetry-maintaining approach circumvents the mesh generation complications inherent in finite element and finite difference techniques, which typically compromise the problem’s natural symmetry, while delivering comparable precision. Performance evaluations reveal nearly linear parallel computational scaling across up to eight processing cores with efficiency rates above 70%, making the method well-suited for multi-core computational platforms. The approach demonstrates particular effectiveness in analyzing torsional stress patterns in thin-walled spherical components under both symmetric and asymmetric boundary scenarios, where traditional grid-based methods encounter discretization and convergence difficulties. The findings offer valuable practical recommendations for material specification and structural design enhancement, especially relevant for pressure vessel and dome structure applications experiencing torsional loads. However, the probabilistic characteristics of the method create statistical uncertainty that requires cautious result interpretation, and computational expenses may surpass those of deterministic approaches for less complex geometries. Engineering analysis of the outcomes provides actionable recommendations for optimizing material utilization and maintaining structural reliability under torsional loading conditions. Full article

To address the lack of robustness against illumination and blurring variations in aerial images of transmission towers, an improved image matching algorithm for aerial images is proposed. The proposed algorithm consists of two main components: an enhanced AKAZE algorithm and an improved three-stage feature matching strategy, which are used for feature point detection and feature matching, respectively. First, the improved AKAZE enhances image contrast using Contrast-Limited Adaptive Histogram Equalization (CLAHE), which highlights target features and improves robustness against environmental interference. Subsequently, the original AKAZE algorithm is employed to detect feature points and construct binary descriptors. Building upon this, an improved three-stage feature matching strategy is proposed to estimate the geometric transformation between image pairs. Specifically, the strategy begins with initial feature matching using the nearest neighbor ratio (NNR) method, followed by outlier rejection via the Grid-based Motion Statistics (GMS) algorithm. Finally, an improved Random Sample Consensus (RANSAC) algorithm computes the transformation matrix, further enhancing matching efficiency. Experimental results demonstrate that the proposed method exceeds the original AKAZE algorithm’s matching accuracy by 4∼15% on different image sets while achieving faster matching speeds. Under real-world conditions with UAV-captured aerial images of transmission towers, the proposed algorithm achieves over 95% matching accuracy, which is higher than other algorithms. Our proposed algorithm enables fast and accurate matching of transmission tower aerial images. Full article

The matching of remote sensing images is a critical and necessary procedure that directly impacts the correctness and accuracy of underwater topography, change detection, digital elevation model (DEM) generation, and object detection. The texture of images becomes weaker with increasing water depth, and this results in matching-extraction failure. To address this issue, a novel method, homography-based motion statistics with an epipolar constraint (HMSEC), is proposed to improve the number, reliability, and robustness of matching points for weak-textured seafloor images. In the matching process of HMSEC, a large number of reliable matching points can be identified from the preliminary matching points based on the motion smoothness assumption and motion statistics. Homography and epipolar geometry are also used to estimate the scale and rotation influences of each matching point in image pairs. The results show that the matching-point numbers for the seafloor and land regions can be significantly improved. In this study, we evaluated this method for the areas of Zhaoshu Island, Ganquan Island, and Lingyang Reef and compared the results to those of the grid-based motion statistics (GMS) method. The increment of matching points reached 2672, 2767, and 1346, respectively. In addition, the seafloor matching points had a wider distribution and reached greater water depths of −11.66, −14.06, and −9.61 m. These results indicate that the proposed method could significantly improve the number and reliability of matching points for seafloor images. Full article

Visual Simultaneous Localization and Mapping (VSLAM) estimates the robot’s pose in three-dimensional space by analyzing the depth variations of inter-frame feature points. Inter-frame feature point mismatches can lead to tracking failure, impacting the accuracy of the mobile robot’s self-localization and mapping. This paper proposes a method for removing mismatches of image features in dynamic scenes in visual SLAM. First, the Grid-based Motion Statistics (GMS) method was introduced for fast coarse screening of mismatched image features. Second, an Adaptive Error Threshold RANSAC (ATRANSAC) method, determined by the internal matching rate, was proposed to improve the accuracy of removing mismatched image features in dynamic and static scenes. Third, the GMS-ATRANSAC method was tested for removing mismatched image features, and experimental results showed that GMS-ATRANSAC can remove mismatches of image features on moving objects. It achieved an average error reduction of 29.4% and 32.9% compared to RANSAC and GMS-RANSAC, with a corresponding reduction in error variance of 63.9% and 58.0%, respectively. The processing time was reduced by 78.3% and 38%, respectively. Finally, the effectiveness of inter-frame feature mismatch removal in the initialization thread of ORB-SLAM2 and the tracking thread of ORB-SLAM3 was verified for the proposed algorithm. Full article

Autonomous mobile robots (AMRs) require SLAM technology for positioning and mapping. Their accuracy and real-time performance are the keys to ensuring that the robot can safely and accurately complete the driving task. The visual SLAM systems based on feature points have high accuracy and robustness but poor real-time performance. A lightweight Visual Odometry (VO) based on Lucas–Kanade (LK) optical flow tracking is proposed. Firstly, a robust key point matching relationship between adjacent images is established by using a uniform motion model and a pyramid-based sparse optical flow tracking algorithm. Then, the grid-based motion statistics algorithm and the random sampling consensus algorithm are used to eliminate the mismatched points in turn. Finally, the proposed algorithm and the ORB-SLAM3 front-end are compared in a dataset to verify the effectiveness of the proposed algorithm. The results show that the proposed algorithm effectively improves the real-time performance of the system while ensuring its accuracy and robustness. Full article

Current research on SLAM can be divided into two parts according to the research scenario: SLAM research in dynamic scenarios and SLAM research in static scenarios. Research is now relatively well established for static environments. However, in dynamic environments, the impact of moving objects leads to inaccurate positioning accuracy and poor robustness of SLAM systems. To address the shortcomings of SLAM systems in dynamic environments, this paper develops a series of solutions to address these problems. First, an attention-based Mask R-CNN network is used to ensure the reliability of dynamic object extraction in dynamic environments. Dynamic feature points are then rejected based on the mask identified by the Mask R-CNN network, and a preliminary estimate of the camera pose is made. Secondly, in order to enhance the picture matching quality and efficiently reject the mismatched points, this paper proposes an image mismatching algorithm incorporating adaptive edge distance with grid motion statistics. Finally, static feature points on dynamic objects are re-added using motion constraints and chi-square tests, and the camera’s pose is re-estimated. The SLAM algorithm of this paper was run on the KITTI and TUM-RGBD datasets, respectively, and the results show that the SLAM algorithm of this paper outperforms the ORB-SLAM2 algorithm for sequences containing more dynamic objects in the KITTI dataset. On the TUM-RGBD dataset, the Dyna-SLAM algorithm increased localization accuracy by an average of 71.94% when compared to the ORB-SLAM2 method, while the SLAM algorithm in this study increased localization accuracy by an average of 78.18% when compared to the ORB-SLAM2 algorithm. When compared to the Dyna-SLAM technique, the SLAM algorithm in this work increased average positioning accuracy by 6.24%, proving that it is superior to Dyna-SLAM. Full article

The image registration and fusion process of image stitching algorithms entails significant computational costs, and the use of robust stitching algorithms with good performance is limited in real-time applications on PCs (personal computers) and embedded systems. Fast image registration and fusion algorithms suffer from problems such as ghosting and dashed lines, resulting in suboptimal display effects on the stitching. Consequently, this study proposes a multi-channel image stitching approach based on fast image registration and fusion algorithms, which enhances the stitching effect on the basis of fast algorithms, thereby augmenting its potential for deployment in real-time applications. First, in the image registration stage, the gridded Binary Robust Invariant Scalable Keypoints (BRISK) method was used to improve the matching efficiency of feature points, and the Grid-based Motion Statistics (GMS) algorithm with a bidirectional rough matching method was used to improve the matching accuracy of feature points. Then, the optimal seam algorithm was used in the image fusion stage to obtain the seam line and construct the fusion area. The seam and transition areas were fused using the fade-in and fade-out weighting algorithm to obtain smooth and high-quality stitched images. The experimental results demonstrate the performance of our proposed method through an improvement in image registration and fusion metrics. We compared our approach with both the original algorithm and other existing methods and achieved significant improvements in eliminating stitching artifacts such as ghosting and discontinuities while maintaining the efficiency of fast algorithms. Full article

Long-term and high-intensity coal mining has led to the increasingly serious surface subsidence and environmental problems. Surface subsidence monitoring plays an important role in protecting the ecological environment of the mining area and the sustainable development of modern coal mines. The development of surveying technology has promoted the acquisition of high-resolution terrain data. The combination of an unmanned aerial vehicle (UAV) point cloud and the structure from motion (SfM) method has shown the potential of collecting multi-temporal high-resolution terrain data in complex or inaccessible environments. The difference of the DEM (DoD) is the main method to obtain the surface subsidence in mining areas. However, the obtained digital elevation model (DEM) needs to interpolate the point cloud into the grid, and this process may introduce errors in complex natural topographic environments. Therefore, a complete three-dimensional change analysis is required to quantify the surface change in complex natural terrain. In this study, we propose a quantitative analysis method of ground subsidence based on three-dimensional point cloud. Firstly, the Monte Carlo simulation statistical analysis was adopted to indirectly evaluate the performance of direct georeferencing photogrammetric products. After that, the operation of co-registration was carried out to register the multi-temporal UAV dense matching point cloud. Finally, the model-to-model cloud comparison (M3C2) algorithm was used to quantify the surface change and reveal the spatio-temporal characteristics of surface subsidence. In order to evaluate the proposed method, four periods of multi-temporal UAV photogrammetric data and a period of airborne LiDAR point cloud data were collected in the Yangquan mining area, China, from 2020 to 2022. The 3D precision map of a sparse point cloud generated by Monte Carlo simulation shows that the average precision in X, Y and Z directions is 44.80 mm, 45.22 and 63.60 mm, respectively. The standard deviation range of the M3C2 distance calculated by multi-temporal data in the stable area is 0.13–0.19, indicating the consistency of multi-temporal photogrammetric data of UAV. Compared with DoD, the dynamic moving basin obtained by the M3C2 algorithm based on the 3D point cloud obtained more real surface deformation distribution. This method has high potential in monitoring terrain change in remote areas, and can provide a reference for monitoring similar objects such as landslides. Full article

In the process of collecting facial images of cattle in the field, some features of the collected images end up going missing due to the changeable posture of the cattle, which makes the recognition accuracy decrease or impossible to recognize. This paper verifies the practical effects of the classical matching algorithms ORB, SURF, and SIFT in bull face matching recognition. The experimental results show that the traditional matching algorithms perform poorly in terms of matching accuracy and matching time. In this paper, a new matching recognition model is constructed. The model inputs the target cattle facial data from different angles into the feature extraction channel, combined with GMS (grid-based motion statistics) algorithm and random sampling consistent algorithm, to achieve accurate recognition of individual cattle, and the recognition process is simple and fast. The recognition accuracy of the model was 85.56% for the Holstein cow face dataset, 82.58% for the Simmental beef cattle, and 80.73% for the mixed Holstein and Simmental beef cattle dataset. The recognition model constructed in the study can achieve individual recognition of cattle in complex environments, has good robustness to matching data, and can effectively reduce the effects of data angle changes and partial features missing in cattle facial recognition. Full article

Problem: The leverage effect plays an important role in finance. However, the statistical test for the presence of the leverage effect is still lacking study. Approach: In this paper, by using high frequency data, we propose a novel procedure to test if the driving Brownian motion of an It$\widehat{\mathrm{o}}$ semi-martingale is correlated to its volatility (referred to as the leverage effect in financial econometrics) over a long time period. The asymptotic setting is based on observations within a long time interval with the mesh of the observation grid shrinking to zero. We construct a test statistic via forming a sequence of Studentized statistics whose distributions are asymptotically normal over blocks of a fixed time span, and then collect the sequence based on the whole data set of a long time span. Result: The asymptotic behaviour of the Studentized statistics was obtained from the cubic variation of the underlying semi-martingale and the asymptotic distribution of the proposed test statistic under the null hypothesis that the leverage effect is absent was established, and we also show that the test has an asymptotic power of one against the alternative hypothesis that the leverage effect is present. Implications: We conducted extensive simulation studies to assess the finite sample performance of the test statistics, and the results show a satisfactory performance for the test. Finally, we implemented the proposed test procedure to a dataset of the SP500 index. We see that the null hypothesis of the absence of the leverage effect is rejected for most of the time period. Therefore, this provides a strong evidence that the leverage effect is a necessary ingredient in modelling high-frequency data. Full article

This paper presents a new method of removing mismatches of redundant points based on oriented fast and rotated brief (ORB) in vision simultaneous localization and mapping (SLAM) systems. On the one hand, the grid-based motion statistics (GMS) algorithm reduces the processing time of key frames with more feature points and greatly increases the robustness of the original algorithm in a complex environment. On the other hand, aiming at the situation that the GMS algorithm is prone to false matching when there are few symmetry feature point pairs, the random sample consensus (RANSAC) algorithm is used to optimize and correct it. Experiments show that the method we propose has an average error correction rate of 28.81% for individual GMS while the time consumed at the same accuracy threshold is reduced by 72.18% on average. At the same time, we compared it to locality preserving matching (LPM) and progressive sample consensus (PROSAC), and it performed the best. Finally, we integrated GMS-RANSAC into the ORB-SLAM2 system for monocular initialization, which results in a significant improvement. Full article

As an all-weather and all-day remote sensing image data source, SAR (Synthetic Aperture Radar) images have been widely applied, and their registration accuracy has a direct impact on the downstream task effectiveness. The existing registration algorithms mainly focus on small sub-images, and there is a lack of available accurate matching methods for large-size images. This paper proposes a high-precision, rapid, large-size SAR image dense-matching method. The method mainly includes four steps: down-sampling image pre-registration, sub-image acquisition, dense matching, and the transformation solution. First, the ORB (Oriented FAST and Rotated BRIEF) operator and the GMS (Grid-based Motion Statistics) method are combined to perform rough matching in the semantically rich down-sampled image. In addition, according to the feature point pairs, a group of clustering centers and corresponding images are obtained. Subsequently, a deep learning method based on Transformers is used to register images under weak texture conditions. Finally, the global transformation relationship can be obtained through RANSAC (Random Sample Consensus). Compared with the SOTA algorithm, our method’s correct matching point numbers are increased by more than 2.47 times, and the root mean squared error (RMSE) is reduced by more than 4.16%. The experimental results demonstrate that our proposed method is efficient and accurate, which provides a new idea for SAR image registration. Full article

The PHD (Probability Hypothesis Density) filter is a sub-optimal multi-target Bayesian filter based on a random finite set, which is widely used in the tracking and estimation of dynamic objects in outdoor environments. Compared with the outdoor environment, the indoor environment space and the shape of dynamic objects are relatively small, which puts forward higher requirements on the estimation accuracy and response speed of the filter. This paper proposes a method for fast and high-precision estimation of the dynamic objects’ velocity for mobile robots in an indoor environment. First, the indoor environment is represented as a dynamic grid map, and the state of dynamic objects is represented by its grid cells state as random finite sets. The estimation of dynamic objects’ speed information is realized by using the measurement-driven particle-based PHD filter. Second, we bound the dynamic grid map to the robot coordinate system and derived the update equation of the state of the particles with the movement of the robot. At the same time, in order to improve the perception accuracy and speed of the filter for dynamic targets, the CS (Current Statistical) motion model is added to the CV (Constant Velocity) motion model, and interactive resampling is performed to achieve the combination of the advantages of the two. Finally, in the Gazebo simulation environment based on ROS (Robot Operating System), the speed estimation and accuracy analysis of the square and cylindrical dynamic objects were carried out respectively when the robot was stationary and in motion. The results show that the proposed method has a great improvement in effect compared with the existing methods. Full article

Path planning can be perceived as a combination of searching and executing the optimal path between the start and destination locations. Deliberative planning capabilities are essential for the motion of autonomous unmanned vehicles in real-world scenarios. There is a challenge in handling the uncertainty concerning the obstacles in a dynamic scenario, thus requiring an intelligent, robust algorithm, with the minimum computational overhead. In this work, a fuzzy gain-based dynamic ant colony optimization (FGDACO) for dynamic path planning is proposed to effectively plan collision-free and smooth paths, with feasible path length and the minimum time. The ant colony system’s pheromone update mechanism was enhanced with a sigmoid gain function for effective exploitation during path planning. Collision avoidance was achieved through the proposed fuzzy logic control. The results were validated using occupancy grids of variable size, and the results were compared against existing methods concerning performance metrics, namely, time and length. The consistency of the algorithm was also analyzed, and the results were statistically verified. Full article