Display of Surfaces from Volume Data
IEEE Computer Graphics and Applications
Fast visualization of plane-like structures in voxel data
Proceedings of the conference on Visualization '02
Computing Smooth Molecular Surfaces
IEEE Computer Graphics and Applications
Speech/Gesture Interface to a Visual-Computing Environment
IEEE Computer Graphics and Applications
Defining, Computing, and Visualizing Molecular Interfaces
VIS '95 Proceedings of the 6th conference on Visualization '95
Parallelizing a Defect Detection and Categorization Application
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
A generalized framework for mining spatio-temporal patterns in scientific data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Techniques for the Visualization of Topological Defect Behavior in Nematic Liquid Crystals
IEEE Transactions on Visualization and Computer Graphics
Detection and Visualization of Defects in 3D Unstructured Models of Nematic Liquid Crystals
IEEE Transactions on Visualization and Computer Graphics
Middleware for data mining applications on clusters and grids
Journal of Parallel and Distributed Computing
A Vision for Cyberinfrastructure for Coastal Forecasting and Change Analysis
GeoSensor Networks
Mining spatial object associations for scientific data
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Direct volume rendering of volumetric protein data
CGI'06 Proceedings of the 24th international conference on Advances in Computer Graphics
Visualization for the Physical Sciences
Computer Graphics Forum
Hi-index | 0.00 |
In this article we explore techniques to detect and visualize features in data from molecular dynamics (MD) simulations. Although the techniques proposed are general, we focus on silicon (Si) atomic systems. The first set of methods use 3D location of atoms. Defects are detected and categorized using local operators and statistical modeling. Our second set of exploratory techniques employ electron density data. This data is visualized to glean the defects. We describe techniques to automatically detect the salient iso-values for iso-surface extraction and designing transfer functions.We compare and contrast the results obtained from both sources of data. Essentially, we find that the methods of defect (feature) detection are at least as robust as those based on the exploration of electron density for Si systems.