Name
Abstract | ||
|---|---|---|
We have previously developed a best merge region-growing approach that integrates nonadjacent region object aggregation with the neighboring region merge process usually employed in region growing segmentation approaches. This approach has been named HSeg, because it provides a hierarchical set of image segmentation results. Up to this point, HSeg considered only global region feature information in the region growing decision process. We present here three new versions of HSeg that include local edge information into the region growing decision process at different levels of rigor. We then compare the effectiveness and processing times of these new versions HSeg with each other and with the original version of HSeg. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IGARSS.2014.6947591 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
decision theory,geophysical image processing,image segmentation,HSeg,global region feature information,hierarchical set of image segmentation,local edge information,merge region-growing segmentation approach,neighboring region merge process,nonadjacent region object aggregation,region growing decision process,Image processing,image analysis,image edge detection,image segmentation | Computer vision,Scale-space segmentation,Pattern recognition,Feature detection (computer vision),Range segmentation,Segmentation,Image texture,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Region growing | Conference |
ISSN | Citations | PageRank |
2153-6996 | 1 | 0.35 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James C. Tilton | 1 | 489 | 34.22 |
| Edoardo Pasolli | 2 | 285 | 17.04 |