Imaging vector fields using line integral convolution
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
Visualizing vector fields using line integral convolution and dye advection
Proceedings of the 1996 symposium on Volume visualization
Image-guided streamline placement
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Simplified representation of vector fields
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
The Cluster Dissection and Analysis Theory FORTRAN Programs Examples
The Cluster Dissection and Analysis Theory FORTRAN Programs Examples
A Phase Field Model for Continuous Clustering on Vector Fields
IEEE Transactions on Visualization and Computer Graphics
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Grid generation and optimization based on centroidal Voronoi tessellations
Applied Mathematics and Computation
Constrained Centroidal Voronoi Tessellations for Surfaces
SIAM Journal on Scientific Computing
Enhanced Spot Noise for Vector Field Visualization
VIS '95 Proceedings of the 6th conference on Visualization '95
Anisotropic Centroidal Voronoi Tessellations and Their Applications
SIAM Journal on Scientific Computing
Recent progress in robust and quality Delaunay mesh generation
Journal of Computational and Applied Mathematics - Special issue: The international symposium on computing and information (ISCI2004)
On centroidal voronoi tessellation—energy smoothness and fast computation
ACM Transactions on Graphics (TOG)
Vector field analysis and visualization through variational clustering
EUROVIS'05 Proceedings of the Seventh Joint Eurographics / IEEE VGTC conference on Visualization
| Hi-index | 0.00 |
A new method for the simplification and the visualization of vector fields is presented based on the notion of Centroidal Voronoi tessellations (CVTýs). A CVT is a special Voronoi tessellation for which the generators of the Voronoi regions in the tessellation are also the centers of mass (or means) with respect to a prescribed density. A distance function in both the spatial and vector spaces is introduced to measure the similarity of the spatially distributed vector fields. Based on such a distance, vector fields are naturally clustered and their simplified representations are obtained. Our method combines simple geometric intuitions with the rigorously established optimality properties of the CVTs. It is simple to describe, easy to understand and implement. Numerical examples are also provided to illustrate the effectiveness and competitiveness of the CVT-based vector simplification and visualization methodology.