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Wooden artworks residing in churches for centuries inevitably suffered from decaying processes such as Wood worm infestation. About 100 years ago, this process was ceased with a polluting coal tar extract 'Carbolineum', a treatment usually applied to railway sleepers. Nowadays, this lead to undesired effects not only of causing dark stains on the surface of the treated sculptures but also in unimpaired release of traces of harmful substances into the atmosphere perceptible as the typical smell of coal tar oil. To resolve this problem, the knowledge of the internal structure could provide a support for this effort. As an example, sculptures from an epitaph in a church in northern Germany were subjected to X-ray and neutron computed tomography (CT). The purpose of using neutrons was to get information about the distribution of the impregnant Carbolineum. It consists of a mixture of polycyclic aromatic hydrocarbons which should increase the absorption of neutrons. In contrast, X-rays are rather poorly absorbed by such substances as compared to the wood matrix. All radiographic approaches
revealed traces of previous conservation measures that were not always documented in detail. Comparing the neutron images with those from the X-ray interrogation some differences became obvious indicating varied material compositions. Nails e.g. were clearly visible in the X-ray images rather than in the neutron ones. Some less visibly stained parts of the sculptures correspond with obviously replaced materials in the inside. These results will be used to identify ways how to treat the infested artworks.
As a matter of fact, avoiding unexpected events with an undesired outcome is an element of survival strategies.
Such events are encountered unexpectedly mainly because they occur rarely. Both, safety and security measures are the main pillars to prevent them by appropriate inspections. Common tools in both areas e.g. are radiological technologies enabling an insight into objects to detect suspicious features without even touching them. Since any of these measures is linked to efforts, costs or even obstructions of ongoing processes, it needs a rationale to invest into an appropriate activity. A putative objection to take action in this direction always could be the question 'when and how often it could happen'. This gave rise to find approaches how to define 'rare events' and how to deal with them. Since they entail both, the frequency of occurrence and the unpleasantness of the possible outcome make them to have something in common with the definition of risk: a combination of the probability of occurrence of harm and the severity of that harm. Tackling putative consequences is one side of the coin whereas understanding the rareness of an event is the other, an aspect that worries but not always fully understood.
As a first step in approaching the subject 'rare events', the putative occurrence rate is considered in terms of probability distribution functions or their cumulative ones, resp. The problems of estimating an incidence of such an event will be tackled subsequently with the problem of assessing the reliability of diagnostic measures.
Any numeric approach of dealing with rare events inevitably remains an ill defined or 'ill-posed' problem that needs additional information for a reasonably satisfying solution. Ways to ease this situation can be found in utilizing additional information, also commonly called prior knowledge, that might be introduced via the Bayesian inference or by regularization algorithms. Simplified models will demonstrate how to apply such tools.
As a consequence, there are ways helping to avoid unexpected ad 'surprises' by taking adequate measures in due time upon the correct perception of certain indications.
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.