Robust Multi-Modal Image Registration for Image Fusion Enhancement in Infrastructure Inspection

被引:1
|
作者
Shahsavarani, Sara [1 ]
Lopez, Fernando [2 ]
Ibarra-Castanedo, Clemente [1 ]
Maldague, Xavier P. V. [1 ]
机构
[1] Univ Laval, Fac Sci & Engn, Dept Elect & Comp Engn, Comp Vis & Syst Lab CVSL, Quebec City, PQ G1V 0A6, Canada
[2] TORNGATS Serv Tech, 200 Boul Parc Technol, Quebec City, PQ G1P 4S3, Canada
基金
加拿大创新基金会; 加拿大自然科学与工程研究理事会;
关键词
IR and VIS image registration; IR and VIS image fusion; feature-points threshold; non-maximum suppression; homography estimation; template matching; self-supervised learning; MUTUAL INFORMATION; AUTOMATIC REGISTRATION; OPTIMIZATION;
D O I
10.3390/s24123994
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页数:18
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