Novel Registration and Fusion Algorithm for Multimodal Railway Images with Different Field of Views

Autor: Baoqing Guo, Xingfang Zhou, Yingzi Lin, Liqiang Zhu, Zujun Yu
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Journal of Advanced Transportation, Vol 2018 (2018)
Druh dokumentu: article
ISSN: 0197-6729
2042-3195
DOI: 10.1155/2018/7836169
Popis: Objects intruding high-speed railway clearance do great threat to running trains. In order to improve accuracy of railway intrusion detection, an automatic multimodal registration and fusion algorithm for infrared and visible images with different field of views is presented. The ratio of the nearest to next nearest distance, geometric, similar triangle, and RANSAC constraints are used to refine the matching SURF feature points successively. Correct matching points are accumulated with multiframe to overcome the insufficient matching points in single image pair. After being registered, an improved Contourlet transform fusion algorithm combined with total variation and local region energy is proposed. Inverse Contourlet transform to low frequency subband coefficient fused with total variation model and high frequency subband coefficients fused with local region energy is used to reconstruct the fused image. The comparison to other 4 popular fusion methods shows that our algorithm has the best comprehensive performance for multimodal railway image fusion.
Databáze: Directory of Open Access Journals
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