Proceedings of the 7th conference on Visualization '96
Tracking scalar features in unstructured datasets
Proceedings of the conference on Visualization '98
IEEE Transactions on Visualization and Computer Graphics
Volume Illustration: Nonphotorealistic Rendering of Volume Models
IEEE Transactions on Visualization and Computer Graphics
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume Tracking Using Higher Dimensional Isosurfacing
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Intelligent Feature Extraction and Tracking for Visualizing Large-Scale 4D Flow Simulations
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Texture information in run-length matrices
IEEE Transactions on Image Processing
Generating time lines with virtual words for time-varying data visualization
Proceedings of the 5th International Symposium on Visual Information Communication and Interaction
Visualization and analysis of 3D time-varying simulations with time lines
Journal of Visual Languages and Computing
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Analyzing, visualizing, and illustrating changes within time-varying volumetric data is challenging due to the dynamic changes occurring between timesteps. The changes and variations in computational fluid dynamic volumes and atmospheric 3D datasets do not follow any particular transformation. Features within the data move at different speeds and directions making the tracking and visualization of these features a difficult task. We introduce a texture-based feature tracking technique to overcome some of the current limitations found in the illustration and visualization of dynamic changes within time-varying volumetric data. Our texture-based technique tracks various features individually and then uses the tracked objects to better visualize structural changes.We show the effectiveness of our texture-based tracking technique with both synthetic and real world time-varying data. Furthermore, we highlight the specific visualization, annotation, registration, and feature isolation benefits of our technique. For instance, we show how our texture-based tracking can lead to insightful visualizations of time-varying data. Such visualizations, more than traditional visualization techniques, can assist domain scientists to explore and understand dynamic changes.