@inproceedings{43a5832e565848a8b80e12e0eaa9497f,
title = "Infrared and Visible Image Registration in Railway Scenarios via Analytical Vanishing Point Constraints",
abstract = "To address the significant modal differences between infrared (IR) and visible (VIS) railway images, this paper introduces a registration method centered on an analytical vanishing point constraint. Within a coarse-to-fine framework, the method robustly estimates the vanishing point from rail lines to analytically derive translation parameters from scaling, which fundamentally simplifies the coarse registration. Subsequently, using the coarse result as an initial value, an iterative optimization solver refines a full 6-parameter affine model to achieve precise alignment. Experimental results on a self-collected dataset demonstrate that the proposed method effectively overcomes modal discrepancies and achieves accurate registration for railway scenarios.",
keywords = "Coarse-to-fine, Image Registration, Infrared and Visible Images, Railway Scenarios, Vanishing Point",
author = "Jianhua Wang and Jun Tian and Ruiming Zheng and Hao Sun and Yunxu Sun and Wei Liu",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 23rd International Conference on Optical Communications and Networks, ICOCN 2025 ; Conference date: 28-07-2025 Through 31-07-2025",
year = "2025",
doi = "10.1109/ICOCN67308.2025.11145489",
language = "英语",
series = "2025 23rd International Conference on Optical Communications and Networks, ICOCN 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 23rd International Conference on Optical Communications and Networks, ICOCN 2025",
address = "美国",
}