Fast Volumetric Data Exploration with Importance-Based Accumulated Transparency Modulation

dc.contributor.authorWan, Yongen_US
dc.contributor.authorHansen, Chucken_US
dc.contributor.editorRuediger Westermann and Gordon Kindlmannen_US
dc.date.accessioned2014-01-29T18:00:19Z
dc.date.available2014-01-29T18:00:19Z
dc.date.issued2010en_US
dc.description.abstractDirect volume rendering techniques have been successfully applied to visualizing volumetric datasets across many application domains. Due to the sensitivity of transfer functions and the complexity of fine-tuning transfer functions, direct volume rendering is still not widely used in practice. For fast volumetric data exploration, we propose Importance-Based Accumulated Transparency Modulation which does not rely on transfer function manipulation. This novel rendering algorithm is a generalization and extension of the Maximum Intensity Difference Accumulation technique. By only modifying the accumulated transparency, the resulted volume renderings are essentially high dynamic range. We show that by using several common importance measures, different features of the volumetric datasets can be highlighted. The results can be easily extended to a high-dimensional importance difference space, by mixing the results from an arbitrary number of importance measures with weighting factors, which all control the final output with a monotonic behavior. With Importance-Based Accumulated Transparency Modulation, the end-user can explore a wide variety of volumetric datasets quickly without the burden of manually setting and adjusting a transfer function.en_US
dc.description.seriesinformationIEEE/ EG Symposium on Volume Graphicsen_US
dc.identifier.isbn978-3-905674-23-1en_US
dc.identifier.issn1727-8376en_US
dc.identifier.urihttps://doi.org/10.2312/VG/VG10/061-068en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation-Display algorithmsen_US
dc.titleFast Volumetric Data Exploration with Importance-Based Accumulated Transparency Modulationen_US
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