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Feature matching of mechanical parts and model diagrams using traditional algorithms heavily relied on detected key points, and the part diagrams are highly affected by rotation angles and shadow reflections. Sparse textures are present in a large number of regions. The accuracy of feature matching was greatly challenged. Aiming at the problem that only a small number of feature points were extracted by traditional algorithms in this case, thus resulting in a low recognition rate, a feature matching method incorporating deep learning was proposed to address this issue. Firstly, the part map was divided into texture-rich and texture-sparse regions using the super-pixel segmentation algorithm. Then, local features were extracted using the SuperPoint and SuperGlue algorithm for texture-rich regions, and global features were extracted using the LoFTR algorithm for texture-sparse regions in order to obtain features with stronger robustness. The features extracted by LoFTR were encoded by geometric convolutional neural networks (GCNNs) to capture the geometric structure information in the image and make them more rotation and translation invariant. Finally, a maximum posteriori sample and consensus (MAGSAC + +) improvement algorithm was introduced to robustly estimate and filter the matching results, eliminating false matches and further improving the matching accuracy. The experimental results show that the F-value is respectively improved by 14. 9%, 23. 1% and 8. 3%, comparing with the scale invariant feature transform (SIFT), speeded-up robust features (SURF) and D2Net matching methods based on traditional algorithms, which are more effective in terms of the number of matching feature points and accuracy and effectively improve the matching performance in complex scenarios, applications and transformations. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Image feature extraction and matching is a critical technique for video and image stitching, visual localization and navigation in underground coal mines. However, underground tunnelling environmental factors, such as low light, uneven illumination, and repetitive textures, result in low-contrast images with indistinct texture information, making feature point extraction difficult and increasing the rate of mismatches. To address these issues, an coalmine image matching method based on multi-level feature fusion enhancement and weighted grid statistics is proposed. Firstly, multi-level feature detection network based on deformable convolution layers and stacked network is adopted for ensuring the rotational invariance of features. Secondly, both the extracted feature points and descriptors are encoded and projected into a high-dimensional space using a feature enhancement module, and the Transformer is then used to enhance the distinguishability of the features. Finally, to address the perceptual confusion problem caused by repetitive textures in underground scenes, a multi-stage matching optimization strategy based on weighted grids is used. This strategy can combine matching quality factors and motion smoothness constraints to filter the mismatches. A lot of experiments were conducted on an underground actual dataset, as well as on two public datasets including LOL and HPatches. The experimental results show that the proposed feature extraction and matching method has higher accuracy and robustness. Specifically, compared with the ORB, SIFT, ASLFeat and Superpoint algorithms, the proposed feature extraction method achieved average accuracy improvements of 33.07%, 69.78%, 17.65% and 33.52% respectively. Compared with the feature matching methods including FLANN, BF+KNN, BF+RANSAC and BF+GMS, the proposed feature matching method achieved average accuracy improvements of 19.66%, 23.26%, 4.16% and 18.46%.

Objectives: Efficient match pair selection and image feature matching directly affect the efficiency of structure from motion (SfM) -based 3D reconstruction for unmanned aerial vehicle (UAV) images. This paper combines the inverted and direct index structure of the vocabulary tree to achieve the speedup of match pair selection and feature matching for UAV images. Methods: First, for match pair selection, vocabulary tree-based image retrieval has been the commonly used technique. However, it depends on the fixed number or fixed ratio threshold for match pair selection, which may cause many redundant match pairs. An adaptive vocabulary tree-based retrieval algorithm is designed for match pair selection by using the word-image index structure and the spatial distribution of similarity scores, and it can avoid the drawback of depending on fixed thresholds. Second, for feature matching, the nearest neighbor searching method attempts to compute the Euclidean distance exhaustively between two sets of feature descriptors, which causes high computational costs and generates high outlier ratios. Thus, a guided feature matching (GFM) algorithm is presented which casts the explicit closest descriptor searching as the direct assignment by using the image-word index structure of the vocabulary tree. Combining the match pair selection and GFM algorithm, an integrated workflow is finally presented to achieve feature matching of both ordered and unordered UAV images with high precision and efficiency. Results: The proposed workflow is verified using four UAV datasets and compared comprehensively with classical nearest neighbor searching algorithms and commercial software packages. Conclusions: The experimental results verify that the proposed method can achieve efficient match pair selection and avoid the problem of retrieving too many or too few match pairs that are usually caused by traditional methods using fixed threshold or number strategies. Without the sacrifice of matching precision, the speedup ratio of direct assign based feature matching ranges from 156 to 228, and competitive accuracy is also obtained from 3D reconstruction compared with the nearest neighbor searching method. [ABSTRACT FROM AUTHOR]

Ship identification plays a critical role in maritime trade and military activities. Current research largely relies on deep learning-based methods, which demand extensive datasets and high-end hardware, often necessitating GPUs. This requirement significantly limits their practical application. Addressing this challenge, this paper introduces an enhanced bag-of-visual-words ( BoVW) model based on classical computer vision techniques for rapid ship identification. The proposed method initially employs SIFT and SURF techniques to extract local features from ship images, followed by rapid matching and fusion of these features. A graph-theoretic approach is then used to determine the regions of interest (ROI) within the image, reducing background interference. Subsequently, clustering algorithms transform features within the ROIs into visual words and construct a visual dictionary. Each image is described using histograms of visual words. The method also employs a spatial pyramid kernel to represent spatial relationships between image features and uses support vector machines (SVM) for supervised learning classification. Key parameters in the model include the size of the visual dictionary and the resolution level. Extensive experiments were conducted to explore these parameters. When the visual dictionary size was set to 300 and the resolution level to 2, the model achieved an accuracy and precision exceeding 96%,validating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problems of outlier, redundant points and fuzzy features in the registration of partially overlapping point clouds, a new registration algorithm for partially overlapping point clouds matching geometric features is proposed. Feature interaction and multilayer perceptron are used to calculate the overlap score and feature salient value of each point to be registered, and extract the salient feature points in the overlapping area. Geometric features are captured according to length and angle, representative feature descriptors are extracted, special geometric feature matching networks are designed, internal values and outlier of key points are identified, and outlier are filtered. The registration results are obtained using weighted singular value decomposition operations. Experimental results show that for ModelNet40 dataset, compared with the benchmark algorithm, the root mean square error and mean absolute error of the proposed algorithm in rotation and translation are reduced by 59%, 45%, 83% and 66% respectively. For the ShapeNetCore dataset, the algorithm is reduced by 63%, 32%, 78%, and 50% in four indicators, respectively. [ABSTRACT FROM AUTHOR]

Aiming at the problem that traditional image registration methods have poor effect in infrared and visible image registration tasks. A feature matching method based on Superpoint + Superglue (2S) composite network was proposed for infrared and visible image registration. The method first used Superpoint's unique feature extraction method to fully extract common features between infrared and visible light images. Secondly, the idea of adding matching constraints and using attention mechanism in Superlube feature matching method was used to give full play to the advantages of neural network and improve the matching efficiency. In the training phase, the method of using self built datasets was used to improve the generalization and accuracy of the neural network. The results show that the repeatability and accuracy scores of traditional registration methods for feature point extraction on three sets of experimental images are (0. 006 7, 0. 006 1), (0. 001 0, 0. 000 8), and (0, 0), respectively. The correct matching logarithms of feature points are 7 pairs, 1 pair, and 0 pairs, with an average number lower than the minimum four matching point pairs required to estimate the transformation matrix. The scores of infrared and visible image registration methods based on Superpoint + Superglue are (0. 240 2, 0. 262 5), (0. 193 9, 0. 172 2), (0. 263 0, 0. 264 4), and the correct matching logarithms of feature points are 252, 165, and 252 pairs. The evaluation index of feature point extraction and the number of correct matching of feature point pairs are significantly increased compared with traditional methods, which can better complete the registration task. [ABSTRACT FROM AUTHOR]

Aiming at the problem of image registration difficulties caused by the significant differences of multi-source remote sensing images due to the sensor type, temporal phase and illumination conditions, this paper proposes a multisource image registration method guided by first-order Gaussian steerable filters. Methods: First, based on the geometric reference information of the image, the overlapping area of the reference image and the image to be aligned in the image space is calculated, the overlapping area is uniformly partitioned with the reference image surface as the reference, the corresponding image block to be aligned is calculated by the rational function model and the digital elevation model, and the affine transformation model is established to geometrically correct the image block to be aligned, so as to realize the coarse registration between the local images. Second, for feature detection, anti-cluster features from accelerated segmentation test improved by the chunking uniformity checking strategy are constructed to obtain a large number of uniformly distributed feature points. And for feature description, a set of multi-scale, multi-directional first-order Gaussian steerable filters are constructed to convolve the image, and by pooling the convolution results to achieve feature dimensionality reduction, a multi-source consistent feature description is constructed. Finally, feature matching is performed based on the nearest-neighbor principle, and high-precision correspondences are obtained by eliminating mismatches. And bundle adjustment is further performed based on the rational function model to calibrate the rational polynomial coefficients of the images to be aligned and geometrically correct the images, so as to realize the fine registration of the images. Results and Conclusions: Experimental results using multiple pairs of satellite multisource images show that the accuracy of the proposed method is better than 1 pixel on multitemporal optical data, optical-infrared data, and 1.5 pixels on optical-synthetic aperture radar data, and the computational efficiency is more than doubled compared to the existing similar methods. [ABSTRACT FROM AUTHOR]

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The onboard sensor information of intelligent driving systems primarily fuses LiDAR and camera data. Accurate and stable extrinsic parameter calibrations offer the basis of effective multi-source information fusion. In order to improve the robustness of the perception system, this paper proposes an extrinsic calibration method for LiDAR and cameras based on feature matching. Firstly, a point cloud data sphere center algorithm and image data ellipse algorithm were proposed to extract point cloud three-dimensional coordinates and pixel two-dimensional coordinates of feature points. Next, the constraint of feature point pairs was established in the LiDAR and camera coordinate systems to construct a nonlinear optimization algorithm. Finally, this nonlinear optimization algorithm was used to optimize the extrinsic parameters of the LiDAR and cameras. Evaluation of the projected LiDAR point cloud onto the image based on the optimized extrinsic parameters yielded transverse and vertical average errors of 3.06 pixels and 1.19 pixels, respectively, in the LiDAR and camera joint calibration. The method proposed in the article reduces the average projection error by 40.8% and the error variance by 56.4% compared to the livox_camera_lidar_calibration method. Its accuracy and robustness are significantly better than the latter.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problem of grasping failure or missing grasping due to the change of target gangue position and posture caused by belt slip, deviation and belt speed fluctuation of belt conveyor when the gangue sorting robot sorts gangue, an improved ORB-FLANN efficient matching method of gangue recognition image and sorting image is proposed. An improved ORB feature point detection method is proposed to detect the feature points in the recognition image and sorting image of coal gangue, so as to realize fast detection of image feature points; An improved FLANN matching algorithm is proposed to match the image feature points to achieve efficient matching between the recognition image of coal gangue and the sorting image. Aiming at the problem of long time and low repetition rate of traditional ORB method for coal gangue image feature detection, an improved ORB feature detection method is proposed to improve the speed and repetition rate of image feature point detection; Aiming at the low accuracy of traditional FLANN matching method for coal gangue image matching, an improved FLANN matching method integrating PROSAC algorithm is proposed to eliminate the wrong feature matching point pairs and improve the accuracy of image matching. The method, SURF feature matching method, HU moment invariant matching method, SIFT feature matching method and ORB feature matching method are applied on the experimental platform of the double mechanical arm truss type gangue sorting robot independently developed by the team to carry out gangue matching experiments under different belt speeds, scales and rotation angles. The results show that the matching rate of the method in this paper is 98.2%, and the matching time is 141 ms. It has the characteristics of high matching rate, good real-time performance and strong robustness, It can meet the requirements of efficient and accurate matching of gangue recognition image and sorting image.

Using radiation-variation insensitivity feature transform (RIFT) to calculate feature descriptors and perform feature matching on the maximum index map (MIM) is time-consuming. To solve this problem, an algorithm named binary descriptor for RIFT (BRIFT) is proposed. First, the phase consistency of the image is calculated and the MIM is obtained. Then, the feature descriptor is calculated and binarized through fast algorithm on MIM. Finally, the descriptor of each feature point is matched based on Hamming distance. Taking the remote sensing images with various geometric distortions and non-linear radiation distortions as the test data, the BRIFT algorithm is compared with other feature matching algorithms including SIFT, BRISK, BRIEF, RIFT. The comparison result shows that the proposed BRIFT algorithm can save about 80% to 90% of time consumption of feature descriptor evaluation and about 50% of feature matching time consumption with a slight loss of matching accuracy, and achieve fast and robust matching of remote sensing images from different sources.

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To enable visual tracking of targets by intermeshing-rotor unmanned helicopters in the presence of unstable global positioning system (GPS) signals, a visual tracking control system for intermeshing-rotor unmanned helicopters is designed. Firstly, a vision module is designed to perform image processing on tracked targets, obtain the pixel coordinates of the center point of the tracked target using oriented FAST and rotated BRIEF (ORB) feature matching and rectangle detection. Thus, the actual position of the center point of the tracked target is solved by the camera model and the rotation matrix. Secondly, a tracking control module is designed with a serial proportional-integral-derivative (PID) plus feed-forward control structure. An open-square controller is used in the position control in order to limit the limit position and maximum acceleration, and an intermeshing-rotor attitude control mechanism is designed in the attitude control. Then, the simulation environment is built by the flight control ground station to simulate and verify the flight control system with the added vision module. The results show that the unmanned helicopter can track the desired position output from the vision module very well. Finally, the experimental validation is conducted under the conditions of the maximum flight speed of 1 m/s, the maximum attitude angle of about 8°, and the maximum error of 0.08 m between the position and the desired position. The experiment demonstrates that the proposed system can fulfill the visual tracking of the intermeshing-rotor unmanned helicopter for the target. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In view of the problems that the extracted feature points of the traditional ORB algorithm tend to accumulate in the texture-rich area and the high false matching rate, which cannot meet the requirements of high-precision positioning, and that the ORB-SLAM3 system cannot build a dense map, this paper proposed an improved ORB-GMS feature matching method based on ORB-SLAM3, and added dense-mapping thread to realize the construction of dense maps. Firstly, the feature points extraction process adopted the quadtree strategy to divide the image frame into several meshes, and extracted the best feature points in each mesh. Then, it replaced the motion smoothing constraint with a statistic that rejected incorrect matches during feature matching, and used a comparison of the number of matching pairs and thresholds within the neighborhood of matching pairs to quickly filter correct matches. Finally, it completed the positional estimation and constructed a dense point cloud map by keyframes and corresponding poses. Testing by RGB-D dataset from TUM, the improved algorithm can extract uniform feature points, the number of matches increases by 64.5% than ORB-SLAM3, increases by 4.7% than the GMS algorithm, and the matching elapsed time decreases by 20.4% than ORB-SLAM3, and decreases by 94.6% than the GMS algorithm, which proves that the improved algorithm is superior in feature point extraction and matching. And compared to the ORB-SLAM3, the accuracy of the improved algorithm increases by 3.75%, thus, demonstrating its feasibility and effectiveness in improving the localization accuracy and building dense maps. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Embedded devices have become visible everywhere,and they are used in a range of security-critical and privacy-sensitive applications.However,recent studies show that many embedded devices have backdoor,of which hard-coded backdoor(password backdoor) is the most common.In the triggering process of password backdoor,strcmp-like functions are necessary and important absolutely.However,the current identification of strcmp-like functions mainly relies on function signature and control flow feature matching.The former can't recognize user-defined strcmp-like functions,and the identify effect is greatly affected by the compile environment.The latter has high false positive rate and false negative rate.To solve the above problems,this paper proposes a novel strcmp-like recognition technology CMPSeek.This method builds a model for strcmp-like function identification based on the analysis of control flow and data flow characteristics,which is used to identify strcmp-like functions in binary programs,and is suitable for stripped binary programs.Furthermore,ARM,MIPS,PPC and x86/64 instruction sets are supported by converting binary codes to the intermediate language representation VEX IR codes.Experimental results show that CMPSeek has better results in accuracy rate and recall rate than FLIRT and SaTC in the absence of source code,function name and other information.

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presented a remote sensing image mosaic algorithm for pavement diseases. There was a dynamic sensed of autonomy in its three components (3 c). The algorithm solved a problem in the detection process, which needed to parameterize the pavement disease information with high-resolution panoramic images. This paper determined the autonomous consciousness of the UAV system in a three-dimensional structure based on target detection and stereo active vision, used an incremental search strategy to triangulate SIFT point features based on geometric constraints, combined the PNP-RANSAC algorithm to find the best matching surface for eliminating the wrong matching and used trigonometric function theory to fuse the images. It realized the fast and accurate splicing of long-thread data sets of diseased pavement. The average feature search time is 43. 45% of that without the incremental search strategy, and the stitching accuracy is over 88%. The experimental data show that the three-component autonomous awareness-based UAV pavement remote sensing image stitching system can effectively cope with long-lane pavement distress images. [ABSTRACT FROM AUTHOR]

A SIFT (scale invariant feature Transform) algorithm and an improved RANSAC (random sampling consistency) algorithm were proposed to solve the problem of error detection and miss detection in forest fire image recognition. The algorithm can greatly improve the matching accuracy and shorten the matching time. In this paper, SIFT algorithm was firstly used to extract feature points in the image, and then by reducing the number of points set in RANSAC, the improved RANSAC algorithm was used to process these feature points and remove the mismatched points to achieve accurate matching. The data set was obtained through burning experiment and simulated by MATLAB. By comparing the matching time and matching accuracy of different algorithms, it was proved that the improved RANSAC algorithm in this paper had the effect of improving detection efficiency for forest fire image recognition. [ABSTRACT FROM AUTHOR]

Objective In this paper, to address sparse feature points and unique epipolar constraints, an adaptive depth constraint-based underwater feature matching (ADC-UFM) scheme is proposed. MethodsBy combining a features from accelerated segment test (FAST) operator with scale invariant feature transform (SIFT) descriptors, the matching accuracy can be dramatically improved. By introducing an underwater refractive factor, the matching constraint model (MCM) can be effectively established, thereby contributing to eliminating mismatched points. The adaptive threshold choosing (ATC) module is finely devised to preserve image feature information in changeable underwater environments to an extreme extent. ResultsComprehensive experiments show that the proposed ADC-UFM scheme can outperform typical matching schemes including SIFT, speeded-up robust features (SURF) and SIFT feature matching based on underwater curve constraint (UCC-SIFT), which not only achieves 85.2% matching accuracy but also meets the real-time requirements. ConclusionThe results of this study can provide a reliable guarantee for subsequent underwater 3D reconstruction based on the binocular vision system.

Objectives: Incremental structure from motion（SfM） has become the widely used workflow for aerial triangulation（AT） of unmanned aerial vehicle （UAV） images. Recently, extensive research has been conducted to improve the efficiency, precision and scalability of SfM-based AT for UAV images. Meanwhile, deep learning-based methods have also been exploited for the geometry processing in the fields of photogrammetry and computer vision, which have been verified with large potential in the AT of UAV images. This paper aims to give a review of recent work in the SfM-based AT for UAV images. Methods:Firstly, the workflow of SfM-based AT is briefly presented in terms of feature matching and geometry solving,in which the former aims to obtain enough and accurate correspondences, and the latter attempts to solve unknown parameters. Secondly, literature review is given for feature matching and geometry solving. For feature matching, classical hand-crafted and recent learning-based methods are presented from the aspects of feature extraction, feature matching and outlier removal. For geometry solving, the principle of SfM-based AT is firstly given with some well-known and widely-used open-source SfM software. Efficiency improvement and large-scale processing are then summarized, which focus on improving the capability of SfM to process large-scale UAV images. Finally, further search is concluded from four aspects, including the change of data acquisition modes, the scalability for large-scale scenes, the development of communication and hardware, and the fusion of deep learning-based methods. Results: The review demonstrates that the existing research promotes the development of SfM-based AT towards the direction of high efficiency, high precision and high robustness, and also promotes the development of both commercial and open-source software packages. Conclusions: Considering the characteristics of UAV images, the efficiency, precision and robustness of SfMbased AT and its application need further improvement and exploitation. This paper could give an extensive conclusion and be a useful reference to the related researchers [ABSTRACT FROM AUTHOR]

In order to solve the problems of small field of view and unclear detail features of monitoring images in coal mines, a multi-view image stitching method in mine is proposed. Firstly, an improved adaptive histogram equalization method with contrast limitation is used to pre-process the images to highlight the image details and improve the contrast. Secondly, the ORB algorithm is used to extract the image feature points, and the improved Brief algorithm is used to calculate the feature descriptors. Thirdly, the K-nearest neighbor(KNN) algorithm is used to achieve rough matching of feature point pairs, and the random sample consensus(RANSAC) algorithm is used to filter and eliminate the mismatched feature point pairs, and the optimal perspective transformation matrix is solved to transform the coordinates of the pixel points of the image to be matched. Finally, the hat function weighted average fusion algorithm is used to stitch and fuse the fixed image and the image to be matched. The experimental results show that compared with the speeded up robust features(SIFT) and KAZE algorithms, the ORB algorithm reduces the number of feature points extracted for a single image by 48% and 33% respectively, which improves the effective feature point extraction capability. The feature point extraction time is reduced by 17% and 34% respectively, which improves the calculation efficiency. The images stitched by this method avoid the phenomenon of cracks and black lines at the joints, the image transition is natural, and the definition is high.

Aiming at the visual odometer of feature point method,the change of illumination and view angle could lead to the instability of feature point extraction,which affects the accuracy of camera pose estimation,a monocular vision odometer modeling method based on deep learning SuperGlue matching algorithm is proposed.Firstly,the feature points are obtained by SuperPoint detector,and the resulting feature points are encoded to obtainvectors containing the coordinates and descriptors of the feature points.Then the more representative descriptors are generated by attentional GNN network.We useSinkhorn algorithm to solve the optimal score distribution matrix.Finally,according to the optimal feature matching,the camera pose is restored,and the ca-mera pose is optimized by using the minimum projection error equation.Experiments show that the proposed algorithm is not only more robust to view angle and light change than the visual odometer based on ORB or SIFT,without back-end optimization,but also the accuracy of absolute trajectory error and relative pose error is greatly improved,thus the feasibility and superiority of the deep learning based SuperGlue matching algorithm in visual slam are further verified.

Copyright of Journal of Architecture & Civil Engineering is the property of Chang'an Daxue Zazhishe and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Aiming at the problems of traditional orb algorithm, such as the distribution of feature points is concentrated, the feature point extraction is unstable and the matching accuracy is decreased when the illumination condition changes, an improved orb algorithm is proposed. Firstly, the Difference of Gaussian is constructed, and then each layer image is divided into several regions. According to the median value of pixels in the region, the thresh old value of each region is calculated, and the feature points of each region a reextracted. Then, the feature points are roughly matched by bidirectional matching algorithm. Finally, a weighted mesh motion statistics algorithm is proposed to optimize the coarse matching results. T he experimental results show that the improved ORB algorithm can make the feature points extraction more uniform, and it can extract feature points stably under the condition of changing illumination. The time- consuming of the algorithm is 10. 7 % slower than the traditional ORB algorithm, and the matching accuracy is improved by 41. 7 %. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the high computational complexity of the existing copy-move image forgery detection algorithm,a copy-move forgery detection algorithm based on group scale-invariant feature transform (SIFT) was proposed.Firstly,the simple linear iterative clustering (SLIC) was utilized to divide the input image into non-overlapping and irregular blocks.Secondly,the structure tensor was introduced to classify each block as flat blocks,edge blocks and corner blocks,and then the SIFT feature points extracted from the block were taken as the block features.Finally,the forgery was located by the inter-class matching of the block features.By means of inter-class matching and feature point matching,the time complexity of the proposed copy-move forgery detection algorithm in feature matching and locating forgery region was effectively reduced while guaranteeing the detection effect.The experimental results show that the accuracy of the proposed algorithm is 97.79%,the recall rate is 90.34%,and the F score is 93.59%,the detecting time for the image with size of 1024×768 is 12.72 s,and the detecting time for the image with size of 3000×2000 was 639.93 s.Compared with the existing copy-move algorithm,the proposed algorithm can locate the forgery region quickly and accurately,and has high robustness.

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Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problems of PCB image edge information missing and carrying a lot of noise in industrial production, the existing de-noising algorithm has poor effect, large amount of calculation and high complexity. Based on this, a PCB image denoising algorithm based on improved NLM is proposed to enhance the denoising quality of PCB image. Firstly, the weight adaptive algorithm based on morphology is used to enhance the PCB image, so that the PCB image retains good edge information. Secondly, the feature matching model is introduced to fuse the enhanced PCB image with the original PCB image. Finally, the PCB image is denoised by improving the weight value of NLM algorithm, and the final denoised image is obtained. Experimental results show that, compared with the existing algorithms, the proposed algorithm retains the edge information of PCB image better, has better denoising effect, significantly improves the image quality, enhances the robustness of the image, improves the calculation speed and reduces the complexity of the algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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