STATISTICAL COMPUTATION OF SALIENT ISO-VALUES

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Date
2002
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Detection of the salient iso-values in a volume dataset is often the first step towards its exploration. An error-and-trail approach is often used; new semi-automatic techniques either make assumptions about their data [4] or present multiple criteria for analysis. Determining if a dataset satisfies an algorithm s assumptions, or the criteria to be used in an analysis are both non-trivial tasks. The use of a dataset s statistical signatures, local higher order moments (LHOMs), to characterize its salient iso-values was presented in [10]. In this paper we propose a computational algorithm that uses LHOMs for expedient estimation of salient iso-values. As LHOMs are model independent statistical signatures our algorithm does not impose any assumptions on the data. Further, the algorithm has a single criterion for characterization of the salient iso-values, and the search for this criterion is easily automated. Examples from medical and computational domains are used to demonstrate the effectiveness of the proposed algorithm.
Description

        
@inproceedings{
:10.2312/VisSym/VisSym02/019-024
, booktitle = {
Eurographics / IEEE VGTC Symposium on Visualization
}, editor = {
D. Ebert and P. Brunet and I. Navazo
}, title = {{
STATISTICAL COMPUTATION OF SALIENT ISO-VALUES
}}, author = {
Tenginakai, Shivaraj
and
Machiraju, Raghu
}, year = {
2002
}, publisher = {
The Eurographics Association
}, ISSN = {
1727-5296
}, ISBN = {
1-58113-536-X
}, DOI = {
/10.2312/VisSym/VisSym02/019-024
} }
Citation