STATISTICAL COMPUTATION OF SALIENT ISO-VALUES
| dc.contributor.author | Tenginakai, Shivaraj | en_US |
| dc.contributor.author | Machiraju, Raghu | en_US |
| dc.contributor.editor | D. Ebert and P. Brunet and I. Navazo | en_US |
| dc.date.accessioned | 2014-01-30T06:50:36Z | |
| dc.date.available | 2014-01-30T06:50:36Z | |
| dc.date.issued | 2002 | en_US |
| dc.description.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. | en_US |
| dc.description.seriesinformation | Eurographics / IEEE VGTC Symposium on Visualization | en_US |
| dc.identifier.isbn | 1-58113-536-X | en_US |
| dc.identifier.issn | 1727-5296 | en_US |
| dc.identifier.uri | https://doi.org/10.2312/VisSym/VisSym02/019-024 | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | STATISTICAL COMPUTATION OF SALIENT ISO-VALUES | en_US |
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