Name
Abstract | ||
|---|---|---|
Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered - How do the boundaries of the watershed operator behave? Which loss function does the watershed operator optimize? How does watershed operator... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LSP.2019.2905155 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Image segmentation,Support vector machines,Image edge detection,Machine learning,Morphology,Vegetation,Forestry | Pattern recognition,Mathematical morphology,Generalization,Support vector machine,Image segmentation,Watershed,Artificial intelligence,Operator (computer programming),Partition (number theory),Random forest,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
26 | 5 | 1070-9908 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aditya Challa | 1 | 3 | 4.10 |
| Sravan Danda | 2 | 3 | 4.10 |
| B. S. Daya Sagar | 3 | 26 | 9.32 |
| L. Najman | 4 | 118 | 7.52 |