Salient iso-surface detection with model-independent statistical signatures

  • Authors:
  • Shivaraj Tenginakai;Jinho Lee;Raghu Machiraju

  • Affiliations:
  • The Ohio State University;The Ohio State University;The Ohio State University

  • Venue:
  • Proceedings of the conference on Visualization '01
  • Year:
  • 2001

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Abstract

Volume graphics has not been accepted for widespread use. One of the inhibiting reasons is the lack of general methods for data-analysis and simple interfaces for data exploration. An error-and-trial iterative procedure is often used to select a desirable transfer function or mine the dataset for salient iso-values. New semi-automatic methods that are also data-centric have shown much promise [1][7]. However, general and robust methods are still needed for data-exploration and analysis. In this paper, we propose general model-independent statistical methods based on central moments of data. Using these techniques we show how salient iso-surfaces at material boundaries can be determined. We provide examples from the medical and computational domain to demonstrate the effectiveness of our methods.