Name
Abstract | ||
|---|---|---|
Remote Sensing is an important source of data about the dynamics of Earth’s land and oceans, but retrieve information from this technique, is a challenge. Segmentation is a traditional method in remote sensing, which have a high computational cost. An alternative to suppress this problem is use parallel approaches, which split the image into tiles, and segment each one individually. However, the divisions among tiles are not natural, which create inconsistent objects. In this work, we extended our previous work, which used non-crisp borders computed based on graph-theory. By applying this non-crisp line cut, we avoid the post-processing of neighboring regions, and therefore speed up the segmentation. |
Year | Venue | Field |
|---|---|---|
2015 | GeoInfo | Computer vision,Scale-space segmentation,Segmentation,Computer science,Image segmentation,Artificial intelligence,Speedup |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anderson Reis Soares | 1 | 0 | 0.34 |
| Thales Sehn Körting | 2 | 3 | 2.42 |
| Leila Maria Garcia Fonseca | 3 | 0 | 0.34 |