Fuzzy feature tracking

Andreas Reh, Aleksandr Amirkhanov, Johann Kastner, Meister Eduard Gröller, Christoph Heinzl
Fuzzy feature tracking
Computers and Graphics, 53(PB):177-184, December 2015.

Information

Abstract

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.

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BibTeX

@article{Red_Andreas_2015_FFT,
  title =      "Fuzzy feature tracking",
  author =     "Andreas Reh and Aleksandr Amirkhanov and Johann Kastner and
               Meister Eduard Gr{"o}ller and Christoph Heinzl",
  year =       "2015",
  abstract =   "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.",
  month =      dec,
  journal =    "Computers and Graphics",
  number =     "PB",
  volume =     "53",
  pages =      "177--184",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2015/Red_Andreas_2015_FFT/",
}