Name
Abstract | ||
|---|---|---|
The design of benchmark imagery for validation of image an notation algorithms is considered. Emphasis is placed on imagery that contains industrial facilities, such as chemical re fineries. An application-level facility ontology is used as a means to define salient objects in the benchmark imagery. Instrinsic and extrinsic scene factors important for comprehensive validation are listed, and variability in the benchmarks discussed. Finally, the pros and cons of three forms of bench mark imagery: real, composite and synthetic, are delineated. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IGARSS.2011.6049340 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
computer vision,facilities,facilities planning,ontologies (artificial intelligence),application-level facility ontology,benchmark imagery,chemical refinery,facility annotation algorithm,image annotation algorithm,industrial facility,Algorithm validation,Benchmark imagery,Benchmark variability,Ontology,Real annotated imagery,Validation using synthetic imagery | Data mining,Ontology,Computer science,Image segmentation,Artificial intelligence,Benchmark (computing),Ontology (information science),Computer vision,Algorithm design,Annotation,Automatic image annotation,Algorithm,Solid modeling | Conference |
ISSN | ISBN | Citations |
2153-6996 | 978-1-4577-1003-2 | 1 |
PageRank | References | Authors |
0.39 | 9 | 13 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Randy S. Roberts | 1 | 17 | 2.91 |
| Paul A. Pope | 2 | 3 | 1.15 |
| Ranga Raju Vatsavai | 3 | 430 | 49.30 |
| Jiang M | 4 | 193 | 26.85 |
| Lloyd F. Arrowood | 5 | 6 | 1.28 |
| Timothy G. Trucano | 6 | 7 | 1.60 |
| Shaun S. Gleason | 7 | 57 | 8.28 |
| Anil Cheriyadat | 8 | 162 | 13.48 |
| Alex Sorokine | 9 | 1 | 0.72 |
| Aggelos K. Katsaggelos | 10 | 3410 | 340.41 |
| Thrasyvoulos N. Pappas | 11 | 745 | 104.83 |
| Lucinda R. Gaines | 12 | 1 | 0.39 |
| Lawrence K. Chilton | 13 | 3 | 0.81 |