Details
| Original language | English |
|---|---|
| Title of host publication | 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, URBAN 2003 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 129-132 |
| Number of pages | 4 |
| ISBN (electronic) | 0780377192, 9780780377196 |
| Publication status | Published - 24 Oct 2011 |
| Event | 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, URBAN 2003 - Berlin, Germany Duration: 22 May 2003 → 23 May 2003 |
Publication series
| Name | 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, URBAN 2003 |
|---|
Abstract
Road junctions are important objects for all traffic related tasks, and are essential e.g. for vehicle navigation systems. They also play a major role in topographic mapping. For automatically capturing road junctions from images models are needed, which describe the main aspects. This paper presents an approach to road junction detection based on raster and vector information. The raster features are similar to the ones used in classification approaches. The vector features are derived from a road junction vector model containing edges as road boarders. The whole feature serves as input to an artificial neural network. The neural classifier decides for search window, whether its central pixel is a part of a road junction or not. The developed junction operator was tested on several black-and-white medium resolution orthoimages. The achieved results demonstrate that such junction models can successfully identify three- and four-arms road junctions.
Keywords
- artificial neural networks, feature extraction, junction detection, object recognition
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Earth and Planetary Sciences(all)
- Geophysics
- Earth and Planetary Sciences(all)
- Space and Planetary Science
- Energy(all)
- Energy Engineering and Power Technology
- Engineering(all)
- Electrical and Electronic Engineering
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2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, URBAN 2003. Institute of Electrical and Electronics Engineers Inc., 2011. p. 129-132 5731014 (2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, URBAN 2003).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Detecting road junctions by artificial neural networks
AU - Barsi, A.
AU - Heipke, C.
PY - 2011/10/24
Y1 - 2011/10/24
N2 - Road junctions are important objects for all traffic related tasks, and are essential e.g. for vehicle navigation systems. They also play a major role in topographic mapping. For automatically capturing road junctions from images models are needed, which describe the main aspects. This paper presents an approach to road junction detection based on raster and vector information. The raster features are similar to the ones used in classification approaches. The vector features are derived from a road junction vector model containing edges as road boarders. The whole feature serves as input to an artificial neural network. The neural classifier decides for search window, whether its central pixel is a part of a road junction or not. The developed junction operator was tested on several black-and-white medium resolution orthoimages. The achieved results demonstrate that such junction models can successfully identify three- and four-arms road junctions.
AB - Road junctions are important objects for all traffic related tasks, and are essential e.g. for vehicle navigation systems. They also play a major role in topographic mapping. For automatically capturing road junctions from images models are needed, which describe the main aspects. This paper presents an approach to road junction detection based on raster and vector information. The raster features are similar to the ones used in classification approaches. The vector features are derived from a road junction vector model containing edges as road boarders. The whole feature serves as input to an artificial neural network. The neural classifier decides for search window, whether its central pixel is a part of a road junction or not. The developed junction operator was tested on several black-and-white medium resolution orthoimages. The achieved results demonstrate that such junction models can successfully identify three- and four-arms road junctions.
KW - artificial neural networks
KW - feature extraction
KW - junction detection
KW - object recognition
UR - http://www.scopus.com/inward/record.url?scp=59649084073&partnerID=8YFLogxK
U2 - 10.1109/DFUA.2003.1219972
DO - 10.1109/DFUA.2003.1219972
M3 - Conference contribution
AN - SCOPUS:59649084073
T3 - 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, URBAN 2003
SP - 129
EP - 132
BT - 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, URBAN 2003
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, URBAN 2003
Y2 - 22 May 2003 through 23 May 2003
ER -