| Autor: |
Di, Yide, Liao, Yun, Zhu, Kaijun, Zhou, Hao, Zhang, Yijia, Duan, Qing, Liu, Junhui, Lu, Mingyu |
| Předmět: |
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| Zdroj: |
Visual Computer; Mar2024, Vol. 40 Issue 3, p1839-1851, 13p |
| Abstrakt: |
The matching of infrared and visible images has a wide range of applications across various fields. However, the large difference between these two types of images poses a significant challenge to achieving accurate feature matching. In this paper, we introduce a novel feature matching method for infrared and visible images, named MIVI. Our proposed multi-stage matching architecture enables the model to capture both fine local feature details and remote dependencies, while our novel composite loss function optimizes the model at each stage and significantly improves the matching accuracy. Qualitative and quantitative experiments demonstrate that MIVI outperforms other excellent algorithms in terms of accuracy. The code will be released at: https://github.com/LiaoYun0x0/MIVI. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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