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dc.contributor.authorTenginakai, Shivarajen_US
dc.contributor.authorMachiraju, Raghuen_US
dc.contributor.editorD. Ebert and P. Brunet and I. Navazoen_US
dc.date.accessioned2014-01-30T06:50:36Z
dc.date.available2014-01-30T06:50:36Z
dc.date.issued2002en_US
dc.identifier.isbn1-58113-536-Xen_US
dc.identifier.issn1727-5296en_US
dc.identifier.urihttp://dx.doi.org/10.2312/VisSym/VisSym02/019-024en_US
dc.description.abstractDetection 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.publisherThe Eurographics Associationen_US
dc.titleSTATISTICAL COMPUTATION OF SALIENT ISO-VALUESen_US
dc.description.seriesinformationEurographics / IEEE VGTC Symposium on Visualizationen_US


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