Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 113-118 |
| Seitenumfang | 6 |
| Fachzeitschrift | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Jahrgang | 34 |
| Publikationsstatus | Veröffentlicht - 2003 |
| Veranstaltung | 2003 ISPRS Workshop on Photogrammetric Image Analysis, PIA 2003 - Munich, Deutschland Dauer: 17 Sept. 2003 → 19 Sept. 2003 |
Abstract
Road junctions are important objects for traffic related tasks, vehicle navigation systems, but also have a major role in topographic mapping. The paper describes an approach of automatic junction detection using raster and vector information: mean and standard deviation of gray values, edges as road borders etc. The derived feature set was used to train a feed-forward neural network, which was the base of the junction operator. The operator decides for a running window about having a road junction or not. The found junctions are marked in the output image. The operator was improved by additional features considering parallelism information of roads in junctions. The developed method was tested on black-and-white medium resolution orthoimages of rural areas. The results demonstrate the ability to find road junctions without preliminary road detection.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Sozialwissenschaften (insg.)
- Geografie, Planung und Entwicklung
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in: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Jahrgang 34, 2003, S. 113-118.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Artificial neural networks for the detection of road junctions in aerial images
AU - Barsi, Arpad
AU - Heipke, Christian
PY - 2003
Y1 - 2003
N2 - Road junctions are important objects for traffic related tasks, vehicle navigation systems, but also have a major role in topographic mapping. The paper describes an approach of automatic junction detection using raster and vector information: mean and standard deviation of gray values, edges as road borders etc. The derived feature set was used to train a feed-forward neural network, which was the base of the junction operator. The operator decides for a running window about having a road junction or not. The found junctions are marked in the output image. The operator was improved by additional features considering parallelism information of roads in junctions. The developed method was tested on black-and-white medium resolution orthoimages of rural areas. The results demonstrate the ability to find road junctions without preliminary road detection.
AB - Road junctions are important objects for traffic related tasks, vehicle navigation systems, but also have a major role in topographic mapping. The paper describes an approach of automatic junction detection using raster and vector information: mean and standard deviation of gray values, edges as road borders etc. The derived feature set was used to train a feed-forward neural network, which was the base of the junction operator. The operator decides for a running window about having a road junction or not. The found junctions are marked in the output image. The operator was improved by additional features considering parallelism information of roads in junctions. The developed method was tested on black-and-white medium resolution orthoimages of rural areas. The results demonstrate the ability to find road junctions without preliminary road detection.
KW - Artificial neural networks
KW - Object recognition
KW - Road junctions
UR - http://www.scopus.com/inward/record.url?scp=33747198560&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:33747198560
VL - 34
SP - 113
EP - 118
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
T2 - 2003 ISPRS Workshop on Photogrammetric Image Analysis, PIA 2003
Y2 - 17 September 2003 through 19 September 2003
ER -