A flow-guided streamline seeding strategy
Proceedings of the conference on Visualization '00
ACM Transactions on Graphics (TOG)
Continuous topology simplification of planar vector fields
Proceedings of the conference on Visualization '01
Visualizing Nonlinear Vector Field Topology
IEEE Transactions on Visualization and Computer Graphics
3D Shape Histograms for Similarity Search and Classification in Spatial Databases
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Shape-Similarity Search of 3D Models by using Enhanced Shape Functions
TPCG '03 Proceedings of the Theory and Practice of Computer Graphics 2003
Surface representations of two- and three-dimensional fluid flow topology
VIS '90 Proceedings of the 1st conference on Visualization '90
Investigating Swirl and Tumble Flow with a Comparison of Visualization Techniques
VIS '04 Proceedings of the conference on Visualization '04
Clifford Convolution And Pattern Matching On Vector Fields
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Deformation Modeling for Robust 3D Face Matching
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Vector field design on surfaces
ACM Transactions on Graphics (TOG)
On the Spatial Statistics of Optical Flow
International Journal of Computer Vision
Dynamic approach for face recognition using digital image skin correlation
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Topology-based visualization of time-dependent 2D vector fields
EGVISSYM'01 Proceedings of the 3rd Joint Eurographics - IEEE TCVG conference on Visualization
Technical Section: Example-based interactive illustration of multi-field datasets
Computers and Graphics
| Hi-index | 0.00 |
Sensors such as video surveillance and weather monitoring systems record a significant amount of dynamic data which are represented by vector fields. We present a novel algorithm to measure the similarity of vector fields using global distributions that capture both vector field properties (e.g., vector orientation) and relational geometric information (e.g., the relative positions of two vectors in the field). We show that such global distributions are capable of distinguishing between vector fields of varying complexity and can be used to quantitatively compare similar fields.