Name
Abstract | ||
|---|---|---|
This paper proposes an approach integrating multiple segmentation methods in a systematic way, which can improve overall accuracy without deteriorating accuracy of highly confident segments of a boundary. A segmentation method produces boundary segments, which are then evaluated with an evaluation function considering pros/cons of the current and next methods to apply. Boundary segments with low confidence are replaced by next method while the other segments are kept. These steps are repeated until all segmentation methods are applied. Coarser and more robust method is applied earlier than the others. The proposed approach is implemented for the segmentation of muscles in the Visible Human color images. A balloon method, a minimum cost path finding method, and a Seeded Region Growing method are integrated. The final segmentation results showed improvements in both overall evaluation and segment-based evaluation. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1117/12.431104 | Proceedings of SPIE |
Keywords | Field | DocType |
image segmentation,integration of multiple segmentation methods,evaluation,visible human | Computer vision,Scale-space segmentation,Segmentation,Computer science,Range segmentation,Evaluation function,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Region growing | Conference |
Volume | ISSN | Citations |
4322 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| dongsung kim | 1 | 0 | 0.68 |
| hanyoung kim | 2 | 0 | 0.34 |
| H S Kang | 3 | 8 | 2.96 |