Mismatching Removal for Feature-Point Matching Based on Triangular Topology Probability Sampling Consensus

He, Zaixing; Shen, Chentao; Wang, Quanyou; Zhao, Xinyue; Jiang, Huilong

doi:10.3390/rs14030706

Cited by 15 publications

(11 citation statements)

References 27 publications

(33 reference statements)

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“…At the same time, the accuracy of the algorithm in this paper is 91%, which is 14% higher than that of RANSAC. At the same time, compared with TSAC [34], FLANN [35], GMS [36], KNN [37], LPM [38], the accuracy of the algorithm in this paper is increased by 7%, 26%, 8%, 30%, and 10%, respectively. In Figure 5, the horizontal axis shows the images of fve different environment categories, and the vertical axis shows the running time.…”

Section: Experimental Results and Analysismentioning

confidence: 80%

“…In order to further verify the running speed of the algorithm in this paper, fve diferent environment images of bear, computer, desk, foor, and building in TUM are used as test images to compare the running time of the algorithm, as shown in Figure 5. In this paper, the accuracy [34] is taken as the evaluation index of algorithm accuracy. Set the slope of the matching line segment [39] between two horizontal matching feature points on the matching image as the standard slope, and then compare the slope of other matching line segments with it.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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Study on Optimization Method of Visual Odometry Based on Feature Matching

Song

Zhao

Ren

et al. 2022

Mathematical Problems in Engineering

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The mismatching of image features affects the calculation of the fundamental matrix and then leads to poor estimation accuracy of SLAM visual odometry. Aiming at the above problems, a visual odometry optimization method based on feature matching is proposed. Firstly, the initial matching set is roughly filtered by the minimum distance threshold method, and then, the relative transformation relationship between images is calculated by the RANSAC algorithm. If it conforms to the transformation relationship, it is an interior point. The iteration result with most interior points is the correct matching result. Then, the homography transformation between images is calculated, and the fundamental matrix is calculated by it. The interior points are determined by epipolar geometric constraints, and the fundamental matrix with the most interior points is obtained. Finally, the effects of the visual odometry optimization algorithm are verified by the TUM data set from two aspects: feature matching and fundamental matrix calculation. The experimental results show that an improved feature matching algorithm can effectively remove mismatched feature points while improving the operation efficiency. At the same time, the accuracy of feature point matching is increased by 15.8%. The fundamental matrix estimation algorithm not only improves the calculation accuracy of the fundamental matrix but also increases the interior point rate by 11.9%. A theoretical basis for improving the accuracy estimation of visual odometry will be provided.

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Section: Experimental Results and Analysismentioning

confidence: 80%

Section: Experimental Results and Analysismentioning

confidence: 99%

Study on Optimization Method of Visual Odometry Based on Feature Matching

Song

Zhao

Ren

et al. 2022

Mathematical Problems in Engineering

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show abstract

“…Based on the invariance of spatial affine transformations, reference [29] utilizes spatial angle ordering as a geometric constraint, constructs neighbourhoods through Delaunay triangulation, and eliminates incorrect correspondences via optimization algorithms. He et al [30] proposed a mismatch removal algorithm based on triangular topology probability sampling consensus. This algorithm generates candidate matches by using a probability sampling approach based on the triangular topology structure and then applies a consistency check to filter out mismatches.…”

Section: Related Workmentioning

confidence: 99%

“…He et al. [30] proposed a mismatch removal algorithm based on triangular topology probability sampling consensus. This algorithm generates candidate matches by using a probability sampling approach based on the triangular topology structure and then applies a consistency check to filter out mismatches.…”

Section: Related Workmentioning

confidence: 99%

Feature matching of remote‐sensing images based on bilateral local–global structure consistency

Chen,

Feng

2023

IET Image Processing

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The goal of feature matching is to establish accurate correspondences between feature points in different images depicting the same scene. To address the polymorphism of local structures, the authors propose a mismatch removal method using bilateral local–global structural consistency. This method incorporates the problem of mismatch removal into the framework of graph matching, constructs a global affinity matrix using local structural similarity and global affine transformation consistency, and optimizes it using a constrained integer quadratic programming method. To comprehensively describe the local structure, the signature quadratic form distance (SQFD) is used to measure the consistency of the neighbourhood structure. Specifically, the weights of edges are constructed based on the SQFD of the local structure, while the matching correctness of nodes and edges between the two graphs is described using local vector similarity. Furthermore, the consistency of the global affine transformation is evaluated by assessing the consistency of the local neighbourhood affine transformation between different corresponding point pairs. In estimating the local affine transformation, a bilateral correction is performed using a total least‐squares (TLS) algorithm to measure the similarity of nodes between the two different graphs. Experimental results demonstrate that the proposed algorithm outperforms state‐of‐the‐art methods in terms of accuracy and effectiveness.

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“…The estimation accuracy of the fundamental matrix is mainly related to the extraction and matching accuracy of the feature points [14][15][16]. Among them, the extraction error is usually caused by the noise.…”

Section: Introductionmentioning

confidence: 99%

A Global Fundamental Matrix Estimation Method of Planar Motion Based on Inlier Updating

Liang

Huo

2022

Sensors

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A fundamental matrix estimation based on matching points is a critical problem in epipolar geometry. In this paper, a global fundamental matrix estimation method based on inlier updating is proposed. Firstly, the coplanar constraint was incorporated into the solution of the fundamental matrix to reduce the number of parameters to be solved. Subsequently, an inlier updating matrix was introduced according to the threshold of the epipolar geometry distance to eliminate the potential outliers and obtain a reliable initial value of the fundamental matrix. On this basis, we employed a four-point iterative method to estimate the fundamental matrix and make it satisfy the rank constraint at the same time. Finally, the epipolar geometry in binocular vision was extended to triple-view, and the fundamental matrix obtained in the previous step was globally optimized by minimizing the coordinate deviation between the intersection point and feature point in each group of images. The experiments show that the proposed fundamental matrix estimation method is robust to noise and outliers. In the attitude measurement, the maximum static error was 0.104° and dynamic measurement error was superior to 0.273°, which improved the reconstruction accuracy of feature points. Indoor images were further used to test the method, and the mean rotation angle error was 0.362°. The results demonstrate that the estimation method proposed in this paper has a good practical application prospect in multi-view 3D reconstruction and visual localization.

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Mismatching Removal for Feature-Point Matching Based on Triangular Topology Probability Sampling Consensus

Cited by 15 publications

References 27 publications

Study on Optimization Method of Visual Odometry Based on Feature Matching

Study on Optimization Method of Visual Odometry Based on Feature Matching

Feature matching of remote‐sensing images based on bilateral local–global structure consistency

A Global Fundamental Matrix Estimation Method of Planar Motion Based on Inlier Updating

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