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 |