Comparative Visualization

Information

• Publication Type: Invited Talk
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: 2014
• Event: IEEE Pacific Visualization Symposium (PacificVis)2014
• Location: Yokohama, Japan
• Conference date: 4. March 2014 – 7. March 2014

Abstract

Visualization uses computer-supported, interactive, visual representations of (abstract) data to amplify cognition. In recent years data complexity and variability has increased considerably. This is due to new data sources as well as the availability of uncertainty, error and tolerance information. Instead of individual objects entire sets, collections, and ensembles are visually investigated. This raises the need for effective comparative visualization approaches. Visual data science and computational sciences provide vast amounts of digital variations of a phenomenon which can be explored through superposition, juxtaposition and explicit difference encoding. A few examples of comparative approaches coming from the various areas of visualization, i.e., scientific visualization, information visualization and visual analytics will be treated in more detail. Comparison and visualization techniques are helpful to carry out parameter studies for the special application area of non-destructive testing using 3D X-ray computed tomography (3DCT). We discuss multi-image views and an edge explorer for comparing and visualizing gray value slices and edges of several datasets simultaneously. Visual steering supports decision making in the presence of alternative scenarios. Multiple, related simulation runs are explored through branching operations. To account for uncertain knowledge about the input parameters, visual reasoning employs entire parameter distributions. This can lead to an uncertainty-aware exploration of (continuous) parameter spaces. VAICo, i.e., Visual Analysis for Image Comparison, depicts differences and similarities in large sets of images. It preserves contextual information, but also allows the user a detailed analysis of subtle variations. The approach identifies local changes and applies cluster analysis techniques to embed them in a hierarchy. The results of this comparison process are then presented in an interactive web application which enables users to rapidly explore the space of differences and drill-down on particular features. Given the amplified data variability, comparative visualization techniques are likely to gain in importance in the future. Research challenges, directions, and issues concerning this innovative area are sketched at the end of the talk.

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BibTeX

@talk{Groeller_2014_CV,
  title =      "Comparative Visualization",
  author =     "Eduard Gr\"{o}ller",
  year =       "2014",
  abstract =   "Visualization uses computer-supported, interactive, visual
               representations of (abstract) data to amplify cognition. In
               recent years data complexity and variability has increased
               considerably. This is due to new data sources as well as the
               availability of uncertainty, error and tolerance
               information. Instead of individual objects entire sets,
               collections, and ensembles are visually investigated. This
               raises the need for effective comparative visualization
               approaches. Visual data science and computational sciences
               provide vast amounts of digital variations of a phenomenon
               which can be explored through superposition, juxtaposition
               and explicit difference encoding. A few examples of
               comparative approaches coming from the various areas of
               visualization, i.e., scientific visualization, information
               visualization and visual analytics will be treated in more
               detail. Comparison and visualization techniques are helpful
               to carry out parameter studies for the special application
               area of non-destructive testing using 3D X-ray computed
               tomography (3DCT). We discuss multi-image views and an edge
               explorer for comparing and visualizing gray value slices and
               edges of several datasets simultaneously. Visual steering
               supports decision making in the presence of alternative
               scenarios. Multiple, related simulation runs are explored
               through branching operations. To account for uncertain
               knowledge about the input parameters, visual reasoning
               employs entire parameter distributions. This can lead to an
               uncertainty-aware exploration of (continuous) parameter
               spaces. VAICo, i.e., Visual Analysis for Image Comparison,
               depicts differences and similarities in large sets of
               images. It preserves contextual information, but also allows
               the user a detailed analysis of subtle variations. The
               approach identifies local changes and applies cluster
               analysis techniques to embed them in a hierarchy. The
               results of this comparison process are then presented in an
               interactive web application which enables users to rapidly
               explore the space of differences and drill-down on
               particular features. Given the amplified data variability,
               comparative visualization techniques are likely to gain in
               importance in the future. Research challenges, directions,
               and issues concerning this innovative area are sketched at
               the end of the talk.",
  event =      "IEEE Pacific Visualization Symposium (PacificVis)2014",
  location =   "Yokohama, Japan",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2014/Groeller_2014_CV/",
}