A rendering framework for multiscale views of 3D models
Proceedings of the 2011 SIGGRAPH Asia Conference
Information theory in computer graphics and visualization
SIGGRAPH Asia 2011 Courses
Automatic Stream Surface Seeding: A Feature Centered Approach
Computer Graphics Forum
Versatile communication algorithms for data analysis
EuroMPI'12 Proceedings of the 19th European conference on Recent Advances in the Message Passing Interface
Progressive splatting of continuous scatterplots and parallel coordinates
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Visual reconstructability as a quality metric for flow visualization
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Effective texture models for three dimensional flow visualization
Proceedings of the 28th Spring Conference on Computer Graphics
Opacity optimization for 3D line fields
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Taming massive distributed datasets: data sampling using bitmap indices
Proceedings of the 22nd international symposium on High-performance parallel and distributed computing
| Hi-index | 0.00 |
The process of visualization can be seen as a visual communication channel where the input to the channel is the raw data, and the output is the result of a visualization algorithm. From this point of view, we can evaluate the effectiveness of visualization by measuring how much information in the original data is being communicated through the visual communication channel. In this paper, we present an information-theoretic framework for flow visualization with a special focus on streamline generation. In our framework, a vector field is modeled as a distribution of directions from which Shannon's entropy is used to measure the information content in the field. The effectiveness of the streamlines displayed in visualization can be measured by first constructing a new distribution of vectors derived from the existing streamlines, and then comparing this distribution with that of the original data set using the conditional entropy. The conditional entropy between these two distributions indicates how much information in the original data remains hidden after the selected streamlines are displayed. The quality of the visualization can be improved by progressively introducing new streamlines until the conditional entropy converges to a small value. We describe the key components of our framework with detailed analysis, and show that the framework can effectively visualize 2D and 3D flow data.