Robust Heterogeneous Model Fitting for Multi-source Image Correspondences-Reference-Cited by-同舟云学术

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Robust Heterogeneous Model Fitting for Multi-source Image Correspondences

Published:2024-02-23 Issue:8 Volume:132 Page:2907-2928
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vis

Author:

Lin Shuyuan^ORCID,Huang Feiran,Lai Taotao,Lai Jianhuang,Wang Hanzi,Weng Jian

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

National Key Research and Development Plan of China

Medical Science and Technology Project of Zhejiang Province

Guangdong Key Laboratory of Data Security and Privacy Preserving

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11263-024-02023-9.pdf

Reference60 articles.

1. Bab-Hadiashar, A., & Hoseinnezhad, R. (2008). Bridging parameter and data spaces for fast robust estimation in computer vision. In Digital image computing: Techniques and applications (pp. 1–8).

2. Bay, H., Tuytelaars, T., & Van Gool, L. (2006). Surf: Speeded up robust features. In Proceedings of the European conference on computer vision (pp. 404–417).

3. DeTone, D., Malisiewicz, T., & Rabinovich, A. (2018). Superpoint: Self-supervised interest point detection and description. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 224–236).

4. Fan, Z., Liu, Y., Liu, Y., Zhang, L., Zhang, J., Sun, Y., & Ai, H. (2022). 3MRS: An effective coarse-to-fine matching method for multimodal remote sensing imagery. Remote Sensing, 14(3), 478.

5. Fan, Z., Zhang, L., Liu, Y., Wang, Q., & Zlatanova, S. (2021). Exploiting high geopositioning accuracy of SAR data to obtain accurate geometric orientation of optical satellite images. Remote Sensing, 13(17), 3535.

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