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. © 2013 IEEE.
|Title of host publication
|2013 IEEE Pacific Visualization Symposium (PacificVis)
|Institute of Electrical and Electronics Engineers (IEEE)
|Number of pages
|Published - Feb 2013
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This research was sponsored by the DOE NNSA Award DE-NA0000740, KUS-C1-016-04 made by King Abdullah Univer-sity of Science and Technology (KAUST), DOE SciDAC Insti-tute of Scalable Data Management Analysis and VisualizationDOE DE-SC0007446, NSF OCI-0906379, NSF IIS-1162013, NIH-1R01GM098151-01.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.