Reliable Matching Results Research Articles

Feature Scalar Field Grid-Guided Optical-Flow Image Matching for Multi-View Images of Asteroid

Images captured by deep space probes exhibit large-scale variations, irregular overlap, and remarkable differences in field of view. These issues present considerable challenges for the registration of multi-view asteroid sensor images. To obtain accurate, dense, and reliable matching results of homonymous points in asteroid images, this paper proposes a new scale-invariant feature matching and displacement scalar field-guided optical-flow-tracking method. The method initially uses scale-invariant feature matching to obtain the geometric correspondence between two images. Subsequently, scalar fields of coordinate differences in the x and y directions are constructed based on this correspondence. Next, interim images are generated using the scalar field grid. Finally, optical-flow tracking is performed based on these interim images. Additionally, to ensure the reliability of the matching results, this paper introduces three methods for eliminating mismatched points: bidirectional optical-flow tracking, vector field consensus, and epipolar geometry constraints. Experimental results demonstrate that the proposed method achieves a 98% matching correctness rate and a root mean square error of 0.25 pixels. By combining the advantages of feature matching and optical-flow field methods, this approach achieves image homonymous point matching results with precision and density. The matching method exhibits robustness and strong applicability for asteroid images with cross-scale, large displacement, and large rotation angles.

UAV image matching of mountainous terrain using the LoFTR deep learning model

In natural terrain scene UAV image matching, traditional feature point-based methods often have problems such as an unstable number of extracted feature points, difficulty in detecting feature points in weak texture areas, uneven distribution, and low robustness. Deep learning-based image matching methods can produce larger and more reasonably distributed matching pairs, so this research paper tries to perform UAV image matching based on a deep learning LoFTR algorithm for natural terrain scenes. The critical technical process was: first, the LoFTR algorithm was used to generate dense feature matching, and then the epipolar line constraints were used to purify the interior points, specifically, this study used the MAGSAC++ method to estimate the fundamental matrix, eliminate the wrong matching pairs, and finally get reliable matching results. In this research paper, six sets of visible images taken by different UAVs equipped with different sensors in the field were selected as experimental data to test the method and were compared and analyzed with the traditional classical SIFT, ASIFT, and AKAZE algorithms and the KeyNet-AdaLAM deep learning method. The experimental results show that the method in this study obtains a dense number of robust matching pairs with uniform spatial distribution in the UAV image matching of natural scenes mainly in mountainous areas, and the comprehensive performance is higher and more advantageous than the comparison methods.

Reliable and Efficient UAV Image Matching via Geometric Constraints Structured by Delaunay Triangulation

Outlier removal is a crucial step in local feature-based unmanned aerial vehicle (UAV) image matching. Inspired by our previous work, this paper proposes a method for reliable and efficient outlier removal in UAV image matching. The inputs of the method are only two images without any other auxiliary data. The core idea is to design local geometric constraints within the neighboring structure via the Delaunay triangulation and use a two-stage method for outlier removal and match refinement. In the filter stage, initial matches are first organized as the Delaunay triangulation (DT) and its corresponding graph, and their dissimilarity scores are computed from the affine-invariant spatial angular order (SAO), which is used to achieve hierarchical outlier removal. In addition, by using the triangle constraint between the refined Delaunay triangulation and its corresponding graph, missed inliers are resumed from match expansion. In the verification stage, retained matches are refined using the RANSAC-based global geometric constraint. Therefore, the two-stage algorithm is termed DTSAO-RANSAC. Finally, using four datasets, DTSAO-RANSAC is comprehensively analyzed and compared with other methods in feature matching and image orientation tests. The experimental results demonstrate that compared with the LO-RANSAC algorithm, DTSAO-RANSAC can achieve efficient outlier removal with speedup ratios ranging from 4 to 16 and, it can provide reliable matching results for image orientation of UAV datasets.

A Blockchain-Based Proxy Re-Encryption With Equality Test for Vehicular Communication Systems

Vehicular communication systems (VCS) are likely to play an increasingly important role in future smart city design, for example by improving road safety and traffic efficiency. However, there are underpinning security and privacy challenges, which may also result in under-utilization of vehicular data. In this paper, we introduce a new cryptographic primitive, namely: blockchain-based proxy re-encryption with equality test. Specifically, the proposed approach is designed to achieve reliable matching results by leveraging smart contracts, as well as efficient data sharing and privacy-preserving by combining the function of public-key encryption with equality test and proxy re-encryption. We also implement a prototype of the proposed approach and evaluate its performance with those of three other competing approaches. A comparative summary with three other competing approaches demonstrate that the proposed approach achieves all four features (i.e., decentralization, flexibility, non-interaction, and re-encryption). Findings from the prototype evaluation (using hyperledger fabric v1.4.2 as the test blockchain platform, and the fabric-sample repository) also demonstrate the utility of the proposed approach in practice.

Hierarchical Point Matching Method Based on Triangulation Constraint and Propagation

Reliable image matching is the basis of image-based three-dimensional (3D) reconstruction. This study presents a quasi-dense matching method based on triangulation constraint and propagation as applied to different types of close-range image matching, such as illumination change, large viewpoint, and scale change. The method begins from a set of sparse matched points that are used to construct an initial Delaunay triangulation. Edge-to-edge matching propagation is then conducted for the point matching. Two types of matching primitives from the edges of triangles with areas larger than a given threshold in the reference image, that is, the midpoints of edges and the intersections between the edges and extracted line segments, are used for the matching. A hierarchical matching strategy is adopted for the above-mentioned primitive matching. The points that cannot be matched in the first stage, specifically those that failed in a gradient orientation descriptor similarity constraint, are further matched in the second stage. The second stage combines the descriptor and the Mahalanobis distance constraints, and the optimal matching subpixel is determined according to an overall similarity score defined for the multiple constraints with different weights. Subsequently, the triangulation is updated using the newly matched points, and the aforementioned matching is repeated iteratively until no new matching points are generated. Twelve sets of close-range images are considered for the experiment. Results reveal that the proposed method has high robustness for different images and can obtain reliable matching results.

Regional fuzzy binocular stereo matching algorithm based on global correlation coding for 3D measurement of rail surface

In view of the uncontrollable imaging quality of the electromagnetic railgun’s rail, based on the existing 3D measurement system, which combines binocular vision with active dot matrix illumination, a regional fuzzy binocular stereo matching algorithm based on global correlation coding is presented. Considering the lack of imaging details and textures and the problem of point target matching, the proposed stereo matching algorithm can get more complete and reliable matching results. Compared with the existing matching algorithms (such as SSD, SIFT, DP, BP, GC, etc.), the proposed matching algorithm can ensure the overall matching accuracy of left and right views, and has higher matching integrity. The implementation of the algorithm is that, the image spatial correlation of target image is firstly decoupled by coding, and then the L1 correlation constraint is calculated for the matched images. The combination of cyclic displacement matrix and random matrix is adopted here encoding the target image, which can make the coding result more robust. Meanwhile, the 3D measurement experiment of rail surface is done under dim light condition. The experimental results show that the matching integrity can reach 99.44 %. Through the experiment, the 3D depth map of the target scene is obtained.

Precise Vehicle Position and Heading Estimation Using a Binary Road Marking Map

Road markings are always present on roads to guide and control traffic. Therefore, they can be used at any time for vehicle localization. Moreover, they can be easily extracted by using light detection and ranging (LIDAR) intensity because they are brightly colored. We propose a vehicle localization method using a 2D road marking grid map. The grid map inserts the map information into the grid directly. Thus, an additional process (such as line detection) is not required and there is no problem due to false detection. We obtained road marking using a 3D LIDAR (Velodyne HDL-32E) and binarized this information to store in the map. Thus, we could reduce the map size significantly. In the previous research, the road marking grid map was used only for position estimation. However, we propose a position-and-heading estimation algorithm using the binary road marking grid map. Accordingly, we derive more precise position estimation results. Moreover, position reliability is an important factor for vehicle localization. Autonomous vehicles may cause accidents if they cannot maintain their lane momentarily. Therefore, we propose an algorithm for evaluating map matching results. Consequently, we can use only reliable matching results and increase position reliability. The experiment was conducted in Gangnam, Seoul, where GPS error occurs largely. In the experimental results, the lateral root mean square (RMS) error was 0.05 m and longitudinal RMS error was 0.08 m. Further, we obtained a position error of less than 50 cm in both lateral and longitudinal directions with a 99% confidence level.

Multi-Stage Matching Approach for Mobile Platform Visual Imagery

Unpredictable texture structure and motion blur continuously exist in mobile platform visual imagery and seriously reduce the similarity between images. Thus, accurate, stable, and well-distributed matches to follow the accurate pose estimation of the platform are difficult to obtain. To solve such problems, an effective image matching method for mobile platform visual imagery is presented in this study. The proposed method includes three steps, namely, standard initial matching, transformation matrices evaluation and matching propagation. Firstly, an oriented FAST and rotated BRIEF (ORB) method was used to obtain the number of matches and the initial projective transformation relationship between an image pair. Secondly, an evaluation function was set to choose the suitable rotation matrix for the image scene. Finally, geometric correspondence matching was utilized to propagate matches and produce additional reliable matching results. The geometric correspondence matching used the geometric relationship between the image pair and found more suitable matches than the standard ORB matching. Comprehensive experiments on TUM and ICL-NUIM dataset images showed that the proposed algorithm performs better in terms of correct matches, satisfactory matching rate, and higher matching accuracy than the standard ORB and ORB-slam2 initial match methods.

Matching Multi-Source Optical Satellite Imagery Exploiting a Multi-Stage Approach

Geometric distortions and intensity differences always exist in multi-source optical satellite imagery, seriously reducing the similarity between images, making it difficult to obtain adequate, accurate, stable, and well-distributed matches for image registration. With the goal of solving these problems, an effective image matching method is presented in this study for multi-source optical satellite imagery. The proposed method includes three steps: feature extraction, initial matching, and matching propagation. Firstly, a uniform robust scale invariant feature transform (UR-SIFT) detector was used to extract adequate and well-distributed feature points. Secondly, initial matching was conducted based on the Euclidean distance to obtain a few correct matches and the initial projective transformation between the image pair. Finally, two matching strategies were used to propagate matches and produce more reliable matching results. By using the geometric relationship between the image pair, geometric correspondence matching found more matches than the initial UR-SIFT feature points. Further probability relaxation matching propagated some new matches around the initial UR-SIFT feature points. Comprehensive experiments on Chinese ZY3 and GaoFen (GF) satellite images revealed that the proposed algorithm performs well in terms of the number of correct matches, correct matching rate, spatial distribution, and matching accuracy, compared to the standard UR-SIFT and triangulation-based propagation method.

An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery

This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM) data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC), which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3), Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results.

A Particle Filter-Based Matching Algorithm With Gravity Sample Vector for Underwater Gravity Aided Navigation

Gravity matching algorithm is a key technique of gravity aided navigation for underwater vehicles. The reliability of traditional single point matching algorithm can be easily affected by environmental disturbance, which results in mismatching and decrease of navigation accuracy. Therefore, a particle filter (PF)-based matching algorithm with gravity sample vector is proposed. The correlation between adjacent sample points of inertial navigation system is considered in the vector matching algorithm in order to solve the mismatching problem. The current sampling point matching result is rectified by the vectors composed by the selected sampling points and matching point. The amount of selected sampling points is determined by the gravity field distribution and the real-time performance of the algorithm. A PF-based on Bayesian estimation is introduced in the proposed method to overcome the divergence disadvantage of the traditional point matching algorithm in some matching areas with obvious gravity variation. Simulation results prove that compared with the traditional methods, the proposed method is robust to the changes of gravity anomaly in the matching areas, with more accurate and reliable matching results.

Multiple Constraints Based Robust Matching of Poor-Texture Close-Range Images for Monitoring a Simulated Landslide

Landslides are one of the most destructive geo-hazards that can bring about great threats to both human lives and infrastructures. Landslide monitoring has been always a research hotspot. In particular, landslide simulation experimentation is an effective tool in landslide research to obtain critical parameters that help understand the mechanism and evaluate the triggering and controlling factors of slope failure. Compared with other traditional geotechnical monitoring approaches, the close-range photogrammetry technique shows potential in tracking and recording the 3D surface deformation and failure processes. In such cases, image matching usually plays a critical role in stereo image processing for the 3D geometric reconstruction. However, the complex imaging conditions such as rainfall, mass movement, illumination, and ponding will reduce the texture quality of the stereo images, bringing about difficulties in the image matching process and resulting in very sparse matches. To address this problem, this paper presents a multiple-constraints based robust image matching approach for poor-texture close-range images particularly useful in monitoring a simulated landslide. The Scale Invariant Feature Transform (SIFT) algorithm was first applied to the stereo images for generation of scale-invariate feature points, followed by a two-step matching process: feature-based image matching and area-based image matching. In the first feature-based matching step, the triangulation process was performed based on the SIFT matches filtered by the Fundamental Matrix (FM) and a robust checking procedure, to serve as the basic constraints for feature-based iterated matching of all the non-matched SIFT-derived feature points inside each triangle. In the following area-based image-matching step, the corresponding points of the non-matched features in each triangle of the master image were predicted in the homologous triangle of the searching image by using geometric constraints, followed by a refinement course with similarity constraint and robust checking. A series of temporal Single-Lens Reflex (SLR) and High-Speed Camera (HSC) stereo images captured during the simulated landslide experiment performed on the campus of Tongji University, Shanghai, were employed to illustrate the proposed method, and the dense and reliable image matching results were obtained. Finally, a series of temporal Digital Surface Models (DSM) in the landslide process were constructed using the close-range photogrammetry technique, followed by the discussion of the landslide volume changes and surface elevation changes during the simulation experiment.

건물의 높이 정보 분석을 위한 WorldView-2 스테레오 영상의 정합점 추출방법 평가

특징점은 주로 높이의 변화가 있는 위치에 존재하여 DSM 생성에 의미 있는 화소일 수 있으며, 정확하고 신뢰할 만한 정합 결과를 도출하는 중요한 역할을 한다. 이러한 특징점을 위성영상 내의 건물에서 추출하고 스테레오 영상 간의 정합을 수행하기 위해 사용자의 주관적인 분석을 통한 방법이 주로 쓰여 왔으나 경제적 및 시간적 비용이 드는 단점이 있다. 이러한 부분을 보완하기 위해 본 연구에서는 건물의 높이 정보를 추출하기 위해서 Harris-affine 특징점 추출기법과 SIFT 서술자를 사용한 스테레오 위성영상의 정합점 추출방법을 제시하였다. Harris-affine 추출기법으로 건물에 존재하는 특징점을 추출하고, 스케일 등의 영향이 적은 SIFT 서술자를 활용하여 효과적으로 정합점을 추출하였다. 또한, 탐색범위를 사용하고 영상 내 정합쌍의 각도를 고려하여 좀 더 효과적인 정합점 추출 방법을 제시하였다. 제안방법으로 추출된 정합점을 사용하여 영상 내에 존재하는 건물의 높이 정보를 실제로 분석하여 제안 방법이 수동 방법과 비교하여 2m 미만의 RMSE 값을 가지는 것을 확인하였다. Interest points are generally located at the pixels where height changes occur. So, interest points can be the significant pixels for DSM generation, and these have the important role to generate accurate and reliable matching results. Manual operation is widely used to extract the interest points and to match stereo satellite images using these for generating height information, but it causes economic and time consuming problems. Thus, a tie point extraction method using Harris-affine technique and SIFT(Scale Invariant Feature Transform) descriptors was suggested to analyze height information of buildings in this study. Interest points on buildings were extracted by Harris-affine technique, and tie points were collected efficiently by SIFT descriptors, which is invariant for scale. Searching window for each interest points was used, and direction of tie points pairs were considered for more efficient tie point extraction method. Tie point pairs estimated by proposed method was used to analyze height information of buildings. The result had RMSE values less than 2m comparing to the height information estimated by manual method.

Shelf Surface Motion Estimation from Repeat Satellite Imagery

In this paper, remote sensing data of Amery ice shelf was used to study Antarctica ice motion and flux problem by a hierarchical image matching method. It combines feature points and grid points to provide a dense, precise and reliable matching result. First, seed points are extracted at the top level of image pyramid using the SIFT algorithm with RANSAC approach to remove mismatches and enhance robustness. These points are used to construct an initial triangulation. Then, feature point and grid point matching are conducted based on the triangle constraint. In the process of hierarchical image matching, the parallaxes from upper levels are transferred to levels beneath with triangle constraint. At last, outliers are detected and removed based on local smooth constraint of parallax. Also, bidirectional image matching method is adopted to verify the matching results and increase the number of matched points. Experiments with Landsat7 images show that the proposed method has the capacity to generate reliable and dense matching results for surface velocity estimation from stereo satellite imagery. Global warming will lead to Amery shelf and glaciers melt and flow rate increase, which can be confirmed by on-site GPS and remote sensing data. Through research the ice shelf flow velocity field, the bottom can calculate the ice flux of this area, and result confirm that the impact of climate for glacier and ice shelf.

A HIERARCHICAL IMAGE MATCHING METHOD FOR STEREO SATELLITE IMAGERY

Abstract. Image matching is an essential and difficult task in digital photogrammetry and computer vision. This paper presents a triangulationbased hierarchical image matching algorithm for stereo satellite imagery. It uses a coarse-to-fine hierarchical strategy and combines feature points and grid points to provide a dense, precise and reliable matching result. First, some seed points are extracted at the top level of image pyramid using the SIFT algorithm with RANSAC approach to remove mismatches and enhance robustness. These points are used to construct an initial triangulation. Then, feature point and grid point matching are conducted based on the triangle constraint. In the process of hierarchical image matching, the parallaxes from upper levels are transferred to levels beneath with triangle constraint and epipolar geometrical constraint. At last, outliers are detected and removed based on local smooth constraint of parallax. Also, bidirectional image matching method is adopted to verify the matching results and increase the number of matched points. Experiments with ALOS images show that the proposed method has the capacity to generate reliable and dense matching results for surface reconstruction from stereo satellite imagery.

A MULTI-VIEW IMAGE MATCHING METHOD FOR FEATURE POINTS BASED ON THE MOVING Z-PLANE CONSTRAINT

Abstract. Focusing on the serious occlusion problem in city images, this paper makes full use of the advantage of multi-view image matching, and proposes a reliable multi-view image matching method based on the moving Z-Plane constraint. It supposes a fictitious plane in the object space, and the plane is divided to regular grid cell (small plane element) by a certain interval (≥ image resolution). By moving the plane to different elevation positions, this algorithm makes feature point projection ray in overall images intersect with the plane, and constrains the candidate points by grid cells in the plane. Feature points which come from different images projection ray in the same grid cell on the plane may be regarded as the matching candidates. It selects the images which matching candidate points by gray similarity constraint to avoid the effect from occlusion image. According to the number of projection ray in the grid cell, this algorithm adopts hierarchy matching strategy of "the best candidate will be matched in the first instant", and uses initial matching results as constraint condition in the latter matching process. Finally, the validity of the algorithm proposed in this paper is verified by the experiments using four UltraCamX (UCX) digital aerial images and the algorithm is shown to have reliable matching results.

Integrated point and edge matching on poor textural images constrained by self-adaptive triangulations

This paper presents an innovative image matching method for reliable and dense image matching on poor textural images, which is the integrated point and edge matching based on the self-adaptive edge-constrained triangulations. Firstly, several seed points and seed edges are obtained on the stereo images, and they are used to construct a pair of initial edge-constrained triangulations on the images. Then, points and edges are matched based on the triangle constraint and other constraints. The newly matched points and edges are inserted into the triangulations and the constrained triangulations are updated dynamically along with the matching propagation. The final results will be the final edge-constrained triangulations generated from the successfully matched points and edges. Experiments using typical space-borne, airborne, and terrestrial images with poor textures revealed that the integrated point and edge matching method based on self-adaptive triangulations is able to produce dense and reliable matching results. Moreover, from the final matched points and edges, 3D points and edges preserving the physical boundaries of objects can be further derived based on photogrammetric techniques, which is ideal for further object modeling applications.

A Triangulation-based Hierarchical Image Matching Method for Wide-Baseline Images

This paper presents a triangulation-based hierarchical image matching method for wide-baseline images. The method includes the following three steps: (a) image orientation by incorporating the SIFT algorithm with the RANSAC approach, (b) feature matching based on the self-adaptive triangle constraint, which includes point-to-point matching and subsequent point-to-area matching, and (c) triangulation constrained dense matching based on the previous matched results. Two new constraints, the triangulation-based disparity constraint and triangulation-based gradient orientation constraint, are developed to alleviate the matching ambiguity for wide-baseline images. A triangulation based affine-adaptive cross-correlation is developed to help find correct matches even in the image regions with large perspective distortions. Experiments using Mars ground wide-baseline images and terrestrial wide-baseline images revealed that the proposed method is capable of generating reliable and dense matching results for terrain mapping and surface reconstruction from the wide-baseline images.

Study on the center of rotation method based on minimum spanning tree matching algorithm for fingerprint recognition

The minimum spanning tree (MST) matching algorithm has been used for searching the partial matching points extracted from fingerprint images. The method, however, has some limitations. To obtain the relationship between the enrolled fingerprints and the input fingerprints the MST is used to generate the tree graph that represents the unique graph for the given minutiae. From the graph, the matching points are estimated. However, the shape of the graph highly depends on the positions of the minutiae. If there are some pseudo minutiae caused by noise, the shape of the graph will be totally different. To overcome the limitations of the MST, we propose the center of rotation method (CRM) that finds true partial matching points. The proposed method is based on the assumption that the input fingerprint minutiae are rigid body. In addition, we employ the polygon wrapping the minutiae for fast matching speed and reliable matching results. In conclusion we have been able to confirm fast performance and high identification ratio by using CRM.

Registration of bone surfaces, extracted from CT-datasets, with 3D ultrasound.

An essential task of computer assisted surgery is the registration of preoperative image data with the coordinate system of the operating room. This can be reached by using intraoperative imaging and registrating preoperative and intraoperative datasets. For intraoperative imaging ultrasound is a powerful tool due to the lack of ionizing radiation and because of its fast, inexpensive and easy data acquisition. We propose a surface volume matching algorithm for the registration of bone surfaces and ultrasound volume data. The bone surface is estimated from the preoperative CT data by taking into account that ultrasound only shows parts of the bone surface. By our method reliable matching results are obtained. They are shown with data of the lumbar spine.