Display of Surfaces from Volume Data
IEEE Computer Graphics and Applications
Generation of transfer functions with stochastic search techniques
Proceedings of the 7th conference on Visualization '96
Semi-automatic generation of transfer functions for direct volume rendering
VVS '98 Proceedings of the 1998 IEEE symposium on Volume visualization
Interactive image cube visualization and analysis
VVS '89 Proceedings of the 1989 Chapel Hill workshop on Volume visualization
Basic research for coloring multichannel MRI data
Proceedings of the conference on Visualization '00
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Proceedings of the conference on Visualization '01
Multidimensional Transfer Functions for Interactive Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
The Transfer Function Bake-Off
IEEE Computer Graphics and Applications
Evaluation of glyph-based multivariate scalar volume visualization techniques
Proceedings of the 6th Symposium on Applied Perception in Graphics and Visualization
Parametric visualization of high resolution correlated multi-spectral features using PCA
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
Generic visual analysis for multi- and hyperspectral image data
Data Mining and Knowledge Discovery
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In this paper we present a new application of the principal component analysis (PCA) to generate multidimensional transfer functions. These transfer functions are needed in the volumetric visualization of spectral data to isolate regions that contain interesting peak-shaped features. Both large and small peaks can be equally important and represent the presence of different chemical elements in a dataset. Principal component analysis separates these peaks in different uncorrelated components and can simultaneously identify spatial patterns. This approach is characterized by the direct linkage between the resulting spectral and spatial components. Our method enables us to create an opacity map from these components. One or more mappings can be selected to highlight features in three-dimensional volume visualization.