A GMS-Guided Approach for 2D Feature Correspondence Selection

Qunfang, Tang; Jia, Wei; He, Xiangjian; Zhang, Qingnian; Liu, Haibo

doi:10.1109/access.2020.2975670

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…The GMS algorithm is an image-matching algorithm that processes a large number of matching points to accomplish high quality and highly robust image matching based on grid-based feature points as neighborhood support estimators [75,76]. The principle of the GMS algorithm is illustrated in Figure 3.…”

Section: Mismatch Elimination Strategy Based On Gmsmentioning

confidence: 99%

PSiamRML: Target Recognition and Matching Integrated Localization Algorithm Based on Pseudo-Siamese Network

Fan,

Yang,

et al. 2023

International Journal of Aerospace Engineering

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The positioning function of unmanned aerial vehicles (UAVs) is a challenging and fundamental research topic and is the premise for UAVs to realize autonomous navigation. The disappearance of satellite signals makes it challenging to achieve accurate positioning. Thus, visual positioning algorithms based on computer vision have been proposed in recent years and these algorithms have produced good results. However, these algorithms have relatively simple functions and cannot perceive the environment. Their versatility is poor, and mismatching often occurs, which affects the positioning accuracy. Aiming to address the need for integrated target recognition, target matching, and positioning of UAVs, we propose an algorithm that integrates the target recognition, matching, and positioning functions by combining the single-shot multibox detector (SSD) algorithm with the deep feature matching algorithm. This algorithm is based on the idea of pseudo-Siamese networks and the SSD algorithm, introducing a deep feature matching method to directly calculate the correspondence between two images. The main idea is to use the VGG network trained by the SSD target recognition algorithm to extract deep features, without any special training for feature matching. Finally, by sharing neural network weights, the integrated design of target recognition and image-matching localization algorithms is achieved. Mismatches between the real-time and reference images are addressed by introducing the grid-based motion statistics algorithm to optimize the matching result and improve the correct matching efficiency of the target. The University-Release dataset was used to compare and analyze the performance of the proposed algorithm to verify its superiority and feasibility. The results show that the matching accuracy of the PSiamRML algorithm is generally good and that it significantly compensates for changes in the contrast, scale, brightness, blur, deformation, and so on, apart from improving the stability and robustness. Finally, a matching test scenario with aerial images captured by an S1000 six-rotor UAV served to verify the effectiveness and practicability of the PSiamRML algorithm.

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Section: Mismatch Elimination Strategy Based On Gmsmentioning

confidence: 99%

PSiamRML: Target Recognition and Matching Integrated Localization Algorithm Based on Pseudo-Siamese Network

Fan,

Yang,

et al. 2023

International Journal of Aerospace Engineering

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“…Existing mismatch elimination algorithms always rely on geometric constraints to remove mismatches [27], [28]. These constraints can be roughly divided into local constraints and global constraints.…”

Section: Introductionmentioning

confidence: 99%

A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints

Xiao

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, we propose an effective method for remote sensing image registration. Point features are robust to remote sensing images with low quality, small overlapping area, and local deformation. Therefore, we extract point features from remote sensing images and convert the problem of remote sensing image registration into the problem of feature point matching. A correspondence set constructed solely on the similar of features often contains many false correspondences or outliers, so our key idea is to remove the mismatches in the initial correspondence set and obtain a stable correspondence through a two-step strategy. First, we use two constraints to construct the optimization model which can solve in linear time. The first constraint is that the topology of the points and their neighbors can be maintained after the spatial transformation. Another constraint is that the feature distance of the correct matches are similar to the neighbors. Then, we design a strategy to increase the number of inliers and raise the precision by a global constraint calculated from the solution in the previous step. Experiments on a variety of remote sensing image datasets demonstrate that our method is more robust and accurate than state-of-the-art methods.

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Feature based remote sensing image registration techniques: a comprehensive and comparative review

Misra

Rohil

Moorthi

et al. 2022

International Journal of Remote Sensing

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A GMS-Guided Approach for 2D Feature Correspondence Selection

Cited by 5 publications

References 36 publications

PSiamRML: Target Recognition and Matching Integrated Localization Algorithm Based on Pseudo-Siamese Network

PSiamRML: Target Recognition and Matching Integrated Localization Algorithm Based on Pseudo-Siamese Network

A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints

Feature based remote sensing image registration techniques: a comprehensive and comparative review

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