Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Construction of vector field hierarchies
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Simplified representation of vector fields
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Collapsing flow topology using area metrics
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A continuous clustering method for vector fields
Proceedings of the conference on Visualization '00
A topology simplification method for 2D vector fields
Proceedings of the conference on Visualization '00
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Scientific Computing: An Introductory Survey
Scientific Computing: An Introductory Survey
Continuous topology simplification of planar vector fields
Proceedings of the conference on Visualization '01
A Phase Field Model for Continuous Clustering on Vector Fields
IEEE Transactions on Visualization and Computer Graphics
Visualizing Vector Field Topology in Fluid Flows
IEEE Computer Graphics and Applications
Discrete multiscale vector field decomposition
ACM SIGGRAPH 2003 Papers
An Approximate Distribution for the Normalized Cut
Journal of Mathematical Imaging and Vision
A Graph Clustering Algorithm Based on Minimum and Normalized Cut
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
New Scalar Measures for Diffusion-Weighted MRI Visualization
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Crowd Flow Segmentation Using a Novel Region Growing Scheme
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Manifold learning of vector fields
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Similarity measure for vector field learning
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A shape derivative based approach for crowd flow segmentation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
| Hi-index | 0.00 |
In this paper, we propose an approach for 2D discrete vector field segmentation based on the Green function and normalized cut. The method is inspired by discrete Hodge Decomposition such that a discrete vector field can be broken down into three simpler components, namely, curl-free, divergence-free, and harmonic components. We show that the Green Function Method (GFM) can be used to approximate the curl-free and the divergence-free components to achieve our goal of the vector field segmentation. The final segmentation curves that represent the boundaries of the influence region of singularities are obtained from the optimal vector field segmentations. These curves are composed of piecewise smooth contours or streamlines. Our method is applicable to both linear and nonlinear discrete vector fields. Experiments show that the segmentations obtained using our approach essentially agree with human perceptual judgement.