用于基础设施检测中图像融合增强的稳健多模态图像注册。

IF 3.4 3区 综合性期刊 Q2 CHEMISTRY, ANALYTICAL
Sensors Pub Date : 2024-06-20 DOI:10.3390/s24123994
Sara Shahsavarani, Fernando Lopez, Clemente Ibarra-Castanedo, Xavier P V Maldague
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引用次数: 0

摘要

高效的多模态图像融合在基础设施的无损评估(NDE)中发挥着重要作用,其中一个重要的挑战是如何精确地将缺陷可视化。虽然自动缺陷检测是一项重大进步,但同时确定表面和地下缺陷的精确位置至关重要。因此,可见光和红外数据融合策略对于获取全面、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了权衡。此外,我们还采用了多模态融合策略来研究图像配准结果的稳健性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Multi-Modal Image Registration for Image Fusion Enhancement in Infrastructure Inspection.

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.

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来源期刊
Sensors
Sensors 工程技术-电化学
CiteScore
7.30
自引率
12.80%
发文量
8430
审稿时长
1.7 months
期刊介绍: Sensors (ISSN 1424-8220) provides an advanced forum for the science and technology of sensors and biosensors. It publishes reviews (including comprehensive reviews on the complete sensors products), regular research papers and short notes. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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