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An effective inliers selection strategy for high-precision image feature matching.

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Title: An effective inliers selection strategy for high-precision image feature matching.
Authors: Xia, Lingnan1 (AUTHOR), Wang, Yi2 (AUTHOR), Jin, Dingfei2,3 (AUTHOR) djin@xtu.edu.cn
Source: Imaging Science Journal. Jun2025, Vol. 73 Issue 4, p474-484. 11p.
Subjects: Image registration, Time complexity, Mathematical models, Image processing, Algorithms
Abstract: Seeking a reliable inliers selection method to image feature matching is a fundamental and important task in image processing and robotic vision. To improve the precision of feature matching on putative matches with heavy outliers, we propose a new strategy to select the inliers from the rough feature correspondence-set. Firstly, we construct the inliers selection problem as a concise mathematical model with linear time complexity based on the local K-nearest neighbour structure preserving (KNNSP). Then, to improve the robustness of this model, we design a threshold decrease strategy for this model to iteratively solve the optimal inliers set. Extensive experiments on various image pairs demonstrate that the proposed method does not need prior information and can be directly used for rigid or non-rigid image matching problems. In addition, compared with other typical image matching algorithms, our method ensures competitive performance in precision and recall. [ABSTRACT FROM AUTHOR]
Copyright of Imaging Science Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. 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.)
Database: Engineering Source
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  Data: An effective inliers selection strategy for high-precision image feature matching.
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Xia%2C+Lingnan%22">Xia, Lingnan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yi%22">Wang, Yi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jin%2C+Dingfei%22">Jin, Dingfei</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> djin@xtu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Imaging+Science+Journal%22">Imaging Science Journal</searchLink>. Jun2025, Vol. 73 Issue 4, p474-484. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Time+complexity%22">Time complexity</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Seeking a reliable inliers selection method to image feature matching is a fundamental and important task in image processing and robotic vision. To improve the precision of feature matching on putative matches with heavy outliers, we propose a new strategy to select the inliers from the rough feature correspondence-set. Firstly, we construct the inliers selection problem as a concise mathematical model with linear time complexity based on the local K-nearest neighbour structure preserving (KNNSP). Then, to improve the robustness of this model, we design a threshold decrease strategy for this model to iteratively solve the optimal inliers set. Extensive experiments on various image pairs demonstrate that the proposed method does not need prior information and can be directly used for rigid or non-rigid image matching problems. In addition, compared with other typical image matching algorithms, our method ensures competitive performance in precision and recall. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Imaging Science Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. 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.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/13682199.2024.2430866
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 474
    Subjects:
      – SubjectFull: Image registration
        Type: general
      – SubjectFull: Time complexity
        Type: general
      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Algorithms
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      – TitleFull: An effective inliers selection strategy for high-precision image feature matching.
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            NameFull: Xia, Lingnan
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            NameFull: Wang, Yi
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              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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