Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/15193
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dc.contributor.authorChen, X-
dc.contributor.authorWu, J-
dc.contributor.authorLi, K-
dc.contributor.authorMeng, H-
dc.date.accessioned2017-09-25T13:35:02Z-
dc.date.available2017-09-21-
dc.date.available2017-09-25T13:35:02Z-
dc.date.issued2017-
dc.identifier.citationInfrared Physics and Technology, 2017en_US
dc.identifier.issn1350-4495-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/15193-
dc.description.abstractActive thermography provides infrared images that contain sub-surface defect information, while visible images only reveal surface information. Mapping in- frared information to visible images o ers more comprehensive visualization for decision-making in rail inspection. However, the common information for regis- tration is limited due to di erent modalities in both local and global level. For example, rail track which has low temperature contrast reveals rich details in visible images, but turns blurry in the infrared counterparts. This paper pro- poses a registration algorithm called Edge-Guided Speeded-Up-Robust-Features (EG-SURF) to address this issue. Rather than sequentially integrating local and global information in matching stage which su ered from buckets e ect, this al- gorithm adaptively integrates local and global information into a descriptor to gather more common information before matching. This adaptability consists of two facets, an adaptable weighting factor between local and global information, and an adaptable main direction accuracy. The local information is extracted using SURF while the global information is represented by shape context from edges. Meanwhile, in shape context generation process, edges are weighted ac- cording to local scale and decomposed into bins using a vector decomposition manner to provide more accurate descriptor. The proposed algorithm is qual- itatively and quantitatively validated using eddy current pulsed thermography scene in the experiments. In comparison with other algorithms, better perfor- mance has been achieved.en_US
dc.description.sponsorshipThis work is supported by Engineering and Physical Sciences Research Coun- cil (EPSRC) [grant numbers EP/K503885/1]. Chaoqing Tang would also like to thank China Scholarship Council for funding his Ph.D study.en_US
dc.language.isoenen_US
dc.subjectRain Inspectionen_US
dc.subjectinfrared and visible image registrationen_US
dc.subjectlocal featureen_US
dc.subjectglobal featureen_US
dc.titleInfrared and Visible Images Registration with Adaptable Local-Global Feature Integration for Rail Inspectionen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.infrared.2017.09.013-
dc.relation.isPartOfInfrared Physics and Technology-
pubs.publication-statusPublished-
Appears in Collections:Dept of Mechanical Aerospace and Civil Engineering Embargoed Research Papers

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