Design galleries: a general approach to setting parameters for computer graphics and animation
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
VIS '97 Proceedings of the 8th conference on Visualization '97
Semi-automatic generation of transfer functions for direct volume rendering
VVS '98 Proceedings of the 1998 IEEE symposium on Volume visualization
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Fast detection of meaningful isosurfaces for volume data visualization
Proceedings of the conference on Visualization '01
Salient iso-surface detection with model-independent statistical signatures
Proceedings of the conference on Visualization '01
Proceedings of the conference on Visualization '01
The Transfer Function Bake-Off
IEEE Computer Graphics and Applications
Visualization in Medicine: Theory, Algorithms, and Applications
Visualization in Medicine: Theory, Algorithms, and Applications
Segmentation and visualization of multivariate features using feature-local distributions
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
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Detection of the salient iso-values in a volume dataset is often the first step towards its exploration. An error-and-trail approach is often used; new semi-automatic techniques either make assumptions about their data [4] or present multiple criteria for analysis. Determining if a dataset satisfies an algorithm's assumptions, or the criteria to be used in an analysis are both non-trivial tasks. The use of a dataset's statistical signatures, local higher order moments (LHOMs), to characterize its salient iso-values was presented in [10]. In this paper we propose a computational algorithm that uses LHOMs for expedient estimation of salient iso-values. As LHOMs are model independent statistical signatures our algorithm does not impose any assumptions on the data. Further, the algorithm has a single criterion for characterization of the salient iso-values, and the search for this criterion is easily automated. Examples from medical and computational domains are used to demonstrate the effectiveness of the proposed algorithm.