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Abstract：
Efficient multi-modal image fusion plays an important role in the non-destructive evaluation (NDE) of infrastructures, where an essential challenge is the precise visualizing of defects. While automatic defect detection represents a significant advancement, the determination of the precise location of both surface and subsurface defects simultaneously is crucial. Hence, visible and infrared data fusion strategies are essential for acquiring comprehensive and complementary information to detect defects across vast structures. This paper proposes an infrared and visible image registration method based on Euclidean evaluation together with a trade-off between key-point threshold and non-maximum suppression. Moreover, we employ a multi-modal fusion strategy to investigate the robustness of our image registration results.
摘要：
高效的多模态图像融合在基础设施的无损评估(NDE)中起着重要作用,其中一个基本的挑战是精确可视化的缺陷。虽然自动检测缺陷代表了一个显著的进步，同时确定表面和亚表面缺陷的精确位置是至关重要的。因此，可见光和红外数据融合策略对于获取全面和互补的信息以检测巨大结构中的缺陷至关重要。本文提出了一种基于欧氏评估的红外和可见光图像配准方法，并在关键点阈值和非最大抑制之间进行权衡。此外，我们采用多模态融合策略来研究图像配准结果的鲁棒性。
