Importance-driven algorithms for scientific visualization
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The Ohio State University / OhioLINK
2005
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=osu1118952958 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-osu11189529582021-08-03T05:50:01Z Importance-driven algorithms for scientific visualization Bordoloi, Udeepta Computer Science Visualization flow vector field volume rendering view selection isosurface. Much progress has been made in the field of visualization over the past few years; but in many situations, it is still possible that the available visualization resources are overwhelmed by the amount of input data. The bottleneck may be the available computational power, storage capacity or available manpower, or a combination of these. In such situations, it is necessary to adapt the algorithms so that they can be run efficiently with less computation, with less space requirements, and with less time and effort from the human user. In this thesis, we present three algorithms that work towards reducing the resource constraints while maintaining the integrity of the visualizations. They are bound by a common underlying theme that all data elements are not equal in the particular visualization context – some are more important than others. We use certain data properties to create “importance“ measures for the data. These measures allow us to control the distribution of resources – computational, storage or human – to different portions of the data. We present a space efficient algorithm for speeding up isosurface extraction. Even though there exist algorithms that can achieve optimal search performance to identify isosurface cells, they prove impractical for large datasets due to a high storage overhead. With the dual goals of achieving fast isosurface extraction and simultaneously reducing the space requirement, we introduce an algorithm based on transform coding. We present a view selection method using a viewpoint goodness measure based on the formulation of entropy from information theory. It can be used as a guide which suggests good viewpoints for further exploration. We generate a view space partitioning, and select one representative view for each partition. Together, this set of views encapsulates the most important and distinct views of the data. We present an interactive global visualization technique for dense vector fields using levels of detail. It combines an error-controlled hierarchical approach and hardware acceleration to produce high resolution visualizations at interactive rates. Users can control the trade-off between computation time and image quality, producing visualizations amenable for situations ranging from high frame-rate previewing to accurate analysis. 2005-07-13 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1118952958 http://rave.ohiolink.edu/etdc/view?acc_num=osu1118952958 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
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NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Computer Science Visualization flow vector field volume rendering view selection isosurface. |
| spellingShingle |
Computer Science Visualization flow vector field volume rendering view selection isosurface. Bordoloi, Udeepta Importance-driven algorithms for scientific visualization |
| author |
Bordoloi, Udeepta |
| author_facet |
Bordoloi, Udeepta |
| author_sort |
Bordoloi, Udeepta |
| title |
Importance-driven algorithms for scientific visualization |
| title_short |
Importance-driven algorithms for scientific visualization |
| title_full |
Importance-driven algorithms for scientific visualization |
| title_fullStr |
Importance-driven algorithms for scientific visualization |
| title_full_unstemmed |
Importance-driven algorithms for scientific visualization |
| title_sort |
importance-driven algorithms for scientific visualization |
| publisher |
The Ohio State University / OhioLINK |
| publishDate |
2005 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=osu1118952958 |
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AT bordoloiudeepta importancedrivenalgorithmsforscientificvisualization |
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