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Importance-driven algorithms for scientific visualization

Bordoloi, Udeepta
http://rave.ohiolink.edu/etdc/view?acc_num=osu1118952958

Abstract Details

2005, Doctor of Philosophy, Ohio State University, Computer and Information Science.
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.
Han-Wei Shen (Advisor)
140 p.
Computer Science
Visualization; flow; vector field; volume rendering; view selection; isosurface.

Recommended Citations

Citations

  • Bordoloi, U. (2005). Importance-driven algorithms for scientific visualization [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1118952958

    APA Style (7th edition)

  • Bordoloi, Udeepta. Importance-driven algorithms for scientific visualization. 2005. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1118952958.

    MLA Style (8th edition)

  • Bordoloi, Udeepta. "Importance-driven algorithms for scientific visualization." Doctoral dissertation, Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1118952958

    Chicago Manual of Style (17th edition)

osu1118952958
921
© 2005, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.