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A. Reh, Ch. Heinzl, E. Gröller et al.:
"Fuzzy feature tracking";
Computers & Graphics, 53 (2015), PB; 177 - 184.

In situ analysis is becoming increasingly important in the evaluation of existing as well as novel materials and components. In this domain, specialists require answers on questions such as: How does a process change internal and external structures of a component? or How do the internal features evolve?In this work, we present a novel integrated visual analysis tool to evaluate series of X-ray Computed Tomography (XCT) data. We therefore process volume datasets of a series of XCT scans, which non-destructively cover the evolution of a process by in situ scans. After the extraction of individual features, a feature tracking algorithm is applied to detect changes of features throughout the series as events. We distinguish between creation, continuation, split, merge and dissipation events. As an explicit tracking is not always possible, we introduce the computation of a Tracking Uncertainty. We visualize the data together with the determined events in multiple linked-views, each emphasizing individual aspects of the 4D-XCT dataset series: A Volume Player and a 3D Data View show the spatial feature information, whereas the global overview of the feature evolution is visualized in the Event Explorer. The Event Explorer allows for interactive exploration and selection of the events of interest. The selection is further used as basis to calculate a Fuzzy Tracking Graph visualizing the global evolution of the features over the whole series.We finally demonstrate the results and advantages of the proposed tool using various real world applications, such as a wood shrinkage analysis and an AlSiC alloy under thermal load. Graphical abstractDisplay Omitted HighlightsWe calculate a Tracking Uncertainty in order to find correlated features.The Event Explorer shows a global overview of events and feature properties.The Fuzzy Tracking Graph is used to track features through all time-steps.The Volume Player shows control elements to traverse the steps of a dataset series.

http://dx.doi.org/10.1016/j.cag.2015.04.001