Publication Type |
pre-print |
School or College |
College of Engineering |
Department |
Computing, School of |
Creator |
Hansen, Charles D. |
Other Author |
Zhou, Liang |
Title |
Transfer function design based on user selected samples for intuitive multivariate volume exploration |
Date |
2013-01-01 |
Description |
Multivariate volumetric datasets are important to both science and medicine. We propose a transfer function (TF) design approach based on user selected samples in the spatial domain to make multivariate volumetric data visualization more accessible for domain users. Specifically, the user starts the visualization by probing features of interest on slices and the data values are instantly queried by user selection. The queried sample values are then used to automatically and robustly generate high dimensional transfer functions (HDTFs) via kernel density estimation (KDE). Alternatively, 2D Gaussian TFs can be automatically generated in the dimensionality reduced space using these samples. With the extracted features rendered in the volume rendering view, the user can further refine these features using segmentation brushes. Interactivity is achieved in our system and different views are tightly linked. Use cases show that our system has been successfully applied for simulation and complicated seismic data sets. |
Type |
Text |
Publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
First Page |
73 |
Last Page |
80 |
Language |
eng |
Bibliographic Citation |
Zhou, L., & Hansen, C. (2013). Transfer function design based on user selected samples for intuitive multivariate volume exploration. IEEE Pacific Visualization Symposium, 6596130, 73-80. |
Rights Management |
(c) 2013 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 |
5,860,344 bytes |
Identifier |
uspace,18362 |
ARK |
ark:/87278/s6089f76 |
Setname |
ir_uspace |
ID |
711378 |
Reference URL |
https://collections.lib.utah.edu/ark:/87278/s6089f76 |