Importance-Driven Focus of Attention

Information

• Publication Type: Journal Paper with Conference Talk
• Workgroup(s)/Project(s):
  • GameTools
  • ExVisation: Expressive Visualization of Volumetric Data
• Date: October 2006
• Journal: IEEE Transactions on Visualization and Computer Graphics
• Volume: 12
• Number: 5
• Lecturer: Ivan Viola
• Pages: 933 – 940
• Keywords: illustrative visualization, interacting with volumetric datasets, optimal viewpoint estimation, focus+context techniques, volume visualization

Abstract

This paper introduces a concept for automatic focusing on features within a volumetric data set. The user selects a focus, i.e., object of interest, from a set of pre-defined features. Our system automatically determines the most expressive view on this feature. A characteristic viewpoint is estimated by a novel information-theoretic framework which is based on the mutual information measure. Viewpoints change smoothly by switching the focus from one feature to another one. This mechanism is controlled by changes in the importance distribution among features in the volume. The highest importance is assigned to the feature in focus. Apart from viewpoint selection, the focusing mechanism also steers visual emphasis by assigning a visually more prominent representation. To allow a clear view on features that are normally occluded by other parts of the volume, the focusing for example incorporates cut-away views.

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BibTeX

@article{vis-foa,
  title =      "Importance-Driven Focus of Attention",
  author =     "Ivan Viola and Miquel Feixas and Mateu Sbert and Eduard
               Gr\"{o}ller",
  year =       "2006",
  abstract =   "This paper introduces a concept for automatic focusing on
               features within a volumetric data set. The user selects a
               focus, i.e., object of interest, from a set of pre-defined
               features. Our system automatically determines the most
               expressive view on this feature. A characteristic viewpoint
               is estimated by a novel information-theoretic framework
               which is based on the mutual information measure. Viewpoints
               change smoothly by switching the focus from one feature to
               another one. This mechanism is controlled by changes in the
               importance distribution among features in the volume. The
               highest importance is assigned to the feature in focus.
               Apart from viewpoint selection, the focusing mechanism also
               steers visual emphasis by assigning a visually more
               prominent representation. To allow a clear view on features
               that are normally occluded by other parts of the volume, the
               focusing for example incorporates cut-away views.",
  month =      oct,
  journal =    "IEEE Transactions on Visualization and Computer Graphics",
  volume =     "12",
  number =     "5",
  pages =      "933--940",
  keywords =   "illustrative visualization, interacting with volumetric
               datasets, optimal viewpoint estimation, focus+context
               techniques, volume visualization",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2006/vis-foa/",
}