An Infrared-Visible Image Registration Method Based on the Constrained Point Feature Article Swipe
Qingqing Li
,
Guangliang Han
,
Peixun Liu
,
Hang Yang
,
Huiyuan Luo
,
Jiajia Wu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21041188
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21041188
It is difficult to find correct correspondences for infrared and visible image registration because of different imaging principles. Traditional registration methods based on the point feature require designing the complicated feature descriptor and eliminate mismatched points, which results in unsatisfactory precision and much calculation time. To tackle these problems, this paper presents an artful method based on constrained point features to align infrared and visible images. The proposed method principally contains three steps. First, constrained point features are extracted by employing an object detection algorithm, which avoids constructing the complex feature descriptor and introduces the senior semantic information to improve the registration accuracy. Then, the left value rule (LV-rule) is designed to match constrained points strictly without the deletion of mismatched and redundant points. Finally, the affine transformation matrix is calculated according to matched point pairs. Moreover, this paper presents an evaluation method to automatically estimate registration accuracy. The proposed method is tested on a public dataset. Among all tested infrared-visible image pairs, registration results demonstrate that the proposed framework outperforms five state-of-the-art registration algorithms in terms of accuracy, speed, and robustness.
Related Topics
Concepts
Affine transformation
Artificial intelligence
Image registration
Robustness (evolution)
Feature (linguistics)
Computer science
Computer vision
Point (geometry)
Point set registration
Pattern recognition (psychology)
Image (mathematics)
Mathematics
Pure mathematics
Geometry
Linguistics
Chemistry
Philosophy
Gene
Biochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s21041188
- https://www.mdpi.com/1424-8220/21/4/1188/pdf?version=1613619697
- OA Status
- gold
- Cited By
- 11
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3126319385
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3126319385Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s21041188Digital Object Identifier
- Title
-
An Infrared-Visible Image Registration Method Based on the Constrained Point FeatureWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-02-08Full publication date if available
- Authors
-
Qingqing Li, Guangliang Han, Peixun Liu, Hang Yang, Huiyuan Luo, Jiajia WuList of authors in order
- Landing page
-
https://doi.org/10.3390/s21041188Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/21/4/1188/pdf?version=1613619697Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/21/4/1188/pdf?version=1613619697Direct OA link when available
- Concepts
-
Affine transformation, Artificial intelligence, Image registration, Robustness (evolution), Feature (linguistics), Computer science, Computer vision, Point (geometry), Point set registration, Pattern recognition (psychology), Image (mathematics), Mathematics, Pure mathematics, Geometry, Linguistics, Chemistry, Philosophy, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2023: 3, 2022: 3, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
56Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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