Generation of transfer functions with stochastic search techniques
Proceedings of the 7th conference on Visualization '96
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
Image-based transfer function design for data exploration in volume visualization
Proceedings of the conference on Visualization '98
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Self-Organizing Maps
Fast detection of meaningful isosurfaces for volume data visualization
Proceedings of the conference on Visualization '01
Tissue Classification Based on 3D Local Intensity Structures for Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
The Transfer Function Bake-Off
IEEE Computer Graphics and Applications
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
An Intelligent System Approach to Higher-Dimensional Classification of Volume Data
IEEE Transactions on Visualization and Computer Graphics
Intelligent Feature Extraction and Tracking for Visualizing Large-Scale 4D Flow Simulations
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Moment based transfer function design for volume rendering
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartIII
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In volume data visualization, the classification is used to determine voxel visibility and is usually carried out by transfer functions that define a mapping between voxel value and color/opacity. The design of transfer functions is a key process in volume visualization applications. However, one transfer function that is suitable for a data set usually dose not suit others, so it is difficult and time-consuming for users to design new proper transfer function when the types of the studied data sets are changed. By introducing neural networks into the transfer function design, a general adaptive transfer function (GATF) is proposed in this paper. Experimental results showed that by using neural networks to guide the transfer function design, the robustness of volume rendering is promoted and the corresponding classification process is optimized.