Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
An introduction to genetic algorithms
An introduction to genetic algorithms
Generation of transfer functions with stochastic search techniques
Proceedings of the 7th conference on Visualization '96
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
Semi-automatic generation of transfer functions for direct volume rendering
VVS '98 Proceedings of the 1998 IEEE symposium on Volume visualization
Image graphs—a novel approach to visual data exploration
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Volume Illustration: Nonphotorealistic Rendering of Volume Models
IEEE Transactions on Visualization and Computer Graphics
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Multiblending: displaying overlapping windows simultaneously without the drawbacks of alpha blending
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An Intelligent System Approach to Higher-Dimensional Classification of Volume Data
IEEE Transactions on Visualization and Computer Graphics
Importance-Driven Feature Enhancement in Volume Visualization
IEEE Transactions on Visualization and Computer Graphics
Interactive clipping techniques for texture-based volume visualization and volume shading
IEEE Transactions on Visualization and Computer Graphics
Focus + context visualization with animation
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Quality enhancement of direct volume rendered images
VG'07 Proceedings of the Sixth Eurographics / Ieee VGTC conference on Volume Graphics
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In this paper, we propose a novel framework which can fuse multiple user selected features in different direct volume rendered images into a comprehensive image according to users’ preference. The framework relies on three techniques, i.e., user voting, genetic algorithm, and image similarity. In this framework, we transform the fusing problem to an optimization problem with a novel energy function which is based on user voting and image similarity. The optimization problem can then be solved by the genetic algorithm. Experimental results on some real volume data demonstrate the effectiveness of our framework.