Name
Abstract | ||
|---|---|---|
We propose a segmentation method for vector fields that employs the accumulated geometric and physical attributes along integral curves to classify their behavior. In particular, we assign to a given spatio-temporal position the attribute value associated with the integral curve initiated at that point. With this attribute information, our segmentation strategy first performs a region classification. Then, connected components are constructed from the derived classification to obtain an initial segmentation. After merging and filtering small segments, we extract and refine the boundaries of the segments. Because points that are correlated by the same integral curve have the same or similar attribute values, the proposed segmentation method naturally generates segments whose boundaries are better aligned with the flow direction. Therefore, additional processing is not required to generate other geometric descriptors within the segmented regions to illustrate the flow behaviors. We apply our method to a number of synthetic and CFD simulation data sets and compare their results with existing methods to demonstrate its effectiveness. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s12650-015-0336-4 | J. Visualization |
Keywords | Field | DocType |
Vector field data,Integral curves,Flow visualization,Segmentation | Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Integral curve,Flow (psychology),Segmentation-based object categorization,Artificial intelligence,Flow visualization,Mathematics | Journal |
Volume | Issue | ISSN |
19 | 3 | 1343-8875 |
Citations | PageRank | References |
3 | 0.38 | 24 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| lei zhang | 1 | 403 | 143.70 |
| Robert S. Laramee | 2 | 1405 | 85.31 |
| David Thompson | 3 | 183 | 18.13 |
| Adrian Sescu | 4 | 15 | 3.36 |
| Guoning Chen | 5 | 320 | 23.72 |