Similarity-Based Exploded Views

  • Marc Ruiz
  • Ivan Viola
  • Imma Boada
  • Stefan Bruckner
  • Miquel Feixas
  • Mateu Sbert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5166)


Exploded views are often used in illustration to overcome the problem of occlusion when depicting complex structures. In this paper, we propose a volume visualization technique inspired by exploded views that partitions the volume into a number of parallel slabs and shows them apart from each other. The thickness of slabs is driven by the similarity between partitions. We use an information-theoretic technique for the generation of exploded views. First, the algorithm identifies the viewpoint which gives the most structured view of the data. Then, the partition of the volume into the most informative slabs for exploding is obtained using two complementary similarity-based strategies. The number of slabs and the similarity parameter are freely adjustable by the user.


Mutual Information Volumetric Data Structure View Entropy Rate Joint Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Marc Ruiz
    • 1
  • Ivan Viola
    • 2
  • Imma Boada
    • 1
  • Stefan Bruckner
    • 3
  • Miquel Feixas
    • 1
  • Mateu Sbert
    • 1
  1. 1.Graphics and Imaging LaboratoryUniversity of GironaSpain
  2. 2.Department of InformaticsUniversity of BergenNorway
  3. 3.Institute of Computer Graphics and AlgorithmsVienna University of TechnologyAustria

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