Statistically quantitative volume visualization

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Publication Type Journal Article
School or College College of Engineering
Department Computing, School of
Program Center for the Simulation of Accidental Fires and Explosions (C-SAFE)
Creator Hansen, Charles D.; Tasdizen, Tolga
Other Author Kniss, Joe M.; Van Uitert, Robert; Stephens, Abraham; Li, Guo-Shi.
Title Statistically quantitative volume visualization
Date 2005
Description Visualization users are increasingly in need of techniques for assessing quantitative uncertainty and error in the images produced. Statistical segmentation algorithms compute these quantitative results, yet volume rendering tools typically produce only qualitative imagery via transfer function-based classification. This paper presents a visualization technique that allows users to interactively explore the uncertainty, risk, and probabilistic decision of surface boundaries. Our approach makes it possible to directly visualize the combined "fuzzy" classification results from multiple segmentations by combining these data into a unified probabilistic data space. We represent this unified space, the combination of scalar volumes from numerous segmentations, using a novel graph-based dimensionality reduction scheme. The scheme both dramatically reduces the dataset size and is suitable for efficient, high quality, quantitative visualization. Lastly, we show that the statistical risk arising from overlapping segmentations is a robust measure for visualizing features and assigning optical properties.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 287
Last Page 294
Subject Volume visualization; Uncertainty; Classification; Risk analysis
Subject LCSH Computer graphics; Visualization
Language eng
Bibliographic Citation Kniss, J., Van Uitert, R., Stephens, A., Li, G-S, Tasdizen, T., & Hansen, C. D. (2005). Statistically quantitative volume visualization. IEEE Visualization, 287-94.
Rights Management (c) 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 1,765,560 bytes
Identifier ir-main,7123
ARK ark:/87278/s69w0ztm
Setname ir_uspace
ID 704437
Reference URL https://collections.lib.utah.edu/ark:/87278/s69w0ztm
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