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X-Ray Testing by Computer Vision

Authors: Domingo Mery;

X-Ray Testing by Computer Vision

Abstract

X-ray imaging has been developed not only for its use in medical imaging for human beings, but also for materials or objects, where the aim is to analyze (nondestructively) those inner parts that are undetectable to the naked eye. Thus, X-ray testing is used to determine if a test object deviates from a given set of specifications. Typical applications are analysis of food products, screening of baggage, inspection of automotive parts, and quality control of welds. In order to achieve efficient and effective X-ray testing, automated and semi-automated systems are being developed to execute this task. In this paper, we present a general overview of computer vision methodologies that have been used in X-ray testing. In addition, we review some techniques that have been applied in certain relevant applications, and we introduce a public database of X-ray images that can be used for testing and evaluation of image analysis and computer vision algorithms. Finally, we conclude that the following: that there are some areas -like casting inspection- where automated systems are very effective, and other application areas -such as baggage screening- where human inspection is still used, there are certain application areas -like weld and cargo inspections- where the process is semi-automatic, and there is some research in areas -including food analysis- where processes are beginning to be characterized by the use of X-ray imaging.

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    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Top 10%
Top 10%
Average
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