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Eingeladener Vortrag
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Reducing risks and confining them to an acceptable level can be regarded as an essential commitment of technical applications for safety purposes and the public security measures.
Rating the efficiency of such measures requires an assessment of the risk that is supposed to be reduced. However, approaching this subject means to face a plethora of existing literature and an ongoing discussion if and how this can be achieved. On one hand, it remains a rather uncertain estimate particularly if it comes to rare interrupting events. On the other hand, making a decision responsibly requires a certain rational base. Several approaches exist for assessing risks, from verbal to quantitative, depending on the individual case. The frequently quoted Delphi technique represents the verbal one, others such as the Bayesian statistics a quantitative one or the consequence/probability matrix in-between.
A problem remains with understanding probability. Even if an event is highly unlikely it may happen right now.
Security research and expertise at the BAM Federal Institute for Materials Research and Testing
(2012)
The ultimate purpose of digital image filtering is to support the visual identification of certain features expressed
by characteristic shapes and patterns. Numerous recipes, algorithms and ready made programs exist nowadays
that predominantly have in common that users have to set certain parameters. Particularly if processing is fast
and shows results rather immediately, the choice of parameters may be guided by making the image 'looking
nice'. However, in practical situations most users are not in a mood to 'play around' with a displayed image,
particularly if they are in a stressy situation as it may encountered in security applications. The requirements for
the application of digital image processing under such circumstances will be discussed with an example of automatic
filtering without manual parameter settings that even entails the advantage of delivering unbiased results.
Safety and security both entail freedom from danger, i.e. unacceptable risk, whatever the cause might be. This
entails an understanding of the term 'risk' that is broadly used in areas such as economics, health, insurance etc.
According a rather popular definition used in the economical sciences for a long time (KNIGHT), risk has a lot
in common with uncertainty while the former is regarded quantifiable and the latter one is not. However, it has
to be taken into account that there are other understandings of 'uncertainty'. Particularly encountering rare
events never experienced before and recent contemporary definitions are weakening Knightfs differentiation.
This is reflected in some recent standard definitions. Attempts have been made to define a risk as common as
possible covering not only societal, ecological and financial areas but also natural and technical, ones. This is of
particular concern in non-destructive testing. This raises the question how far this can be achieved in a common
understanding, i.e. the discussion on this term seems not at all finished yet.
It is a common ambition to lower the risk by several actions including detection technologies. This entails that a
risk could be estimated somehow. The existence of numerous approaches indicates the complexity of this question.
The EFNDT (European Federation for Non-Destructive Testing) Working Group 5 took a commitment
also to tackle this central aspect of safety and security in its understanding as a bridging forum between these
two areas.
Stochastic artefacts are frequently encountered in digital radiography and tomography with neutrons. Most obviously, they are caused by ubiquitous scattered radiation hitting the CCD-sensor. They appear as scattered dots and, at higher frequency of occurrence, they may obscure the image. Some of these dotted interferences vary with time, however, a large portion of them remains persistent so the problem cannot be resolved by collecting stacks of images and to merge them to a median image. The situation becomes even worse in computed tomography (CT) where each artefact causes a circular pattern in the reconstructed plane. Therefore, these stochastic artefacts have to be removed completely and automatically while leaving the original image content untouched. A simplified image acquisition and artefact removal tool was developed at BAM and is available to interested users. Furthermore, an algorithm complying with all the requirements mentioned above was developed that reliably removes artefacts that could even exceed the size of a single pixel without affecting other parts of the image. It consists of an iterative two-step algorithm adjusting pixel values within a 3 × 3 matrix inside of a 5 × 5 kernel and the centre pixel only within a 3 × 3 kernel, resp. It has been applied to thousands of images obtained from the NECTAR facility at the FRM II in Garching, Germany, without any need of a visual control. In essence, the procedure consists of identifying and tackling asymmetric intensity distributions locally with recording each treatment of a pixel. Searching for the local asymmetry with subsequent correction rather than replacing individually identified pixels constitutes the basic idea of the algorithm. The efficiency of the proposed algorithm is demonstrated with a severely spoiled example of neutron radiography and tomography as compared with median filtering, the most convenient alternative approach by visual check, histogram and power spectra analysis.