Representative Views and Paths for Volume Models

  • Pere-Pau Vázquez
  • Eva Monclús
  • Isabel Navazo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5166)


Volume data models are becoming larger and larger as the capture technology improves. Thus, their visualization requires high computational power. The automatic presentation of volume models through representative images and/or exploration paths becomes more and more useful. Representative views are also useful for document illustration, fast data quality evaluation, or model libraries documentation. Exploration paths are also useful for video demonstrations and previsualization of captured data. In this paper we present a fast, adaptive method for the selection of representative views and the automatic generation of exploration paths for volume models. Our algorithm is based on multi-scale entropy and algorithmic complexity. These views and paths reveal informative parts of a model given a certain transfer function. We show that our method is simple and easy to incorporate in medical visualization tools.


Adaptive Method Volume Model Good View Kolmogorov Complexity View Selection 
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

  • Pere-Pau Vázquez
    • 1
  • Eva Monclús
    • 2
  • Isabel Navazo
    • 1
  1. 1.Modeling, Visualization, and Graphics Interaction Group Dep. LSIUniversitat Politècnica de Catalunya (UPC)Spain
  2. 2.Institut de Robòtica i Informàtica IndustrialUPC-CSIC 

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