TY - GEN
T1 - Automatic road extraction from printed maps
AU - Callier, Sebastien
AU - Saito, Hideo
PY - 2011/12/1
Y1 - 2011/12/1
N2 - Raster maps are widely available in the everyday life, and can contain a huge amount of information of any kind using labels, pictograms, or color code e.g. But due to those overlapping features, it's not an easy task to extract roads from those maps. Many methods use user input to achieve this goal. In this paper we focus on an automated method to extract roads by using color segmentation and linear features detection to search for seed points having a high probability to belong to roads. Those seeds are then expanded before choosing to keep or to discard the extracted element.
AB - Raster maps are widely available in the everyday life, and can contain a huge amount of information of any kind using labels, pictograms, or color code e.g. But due to those overlapping features, it's not an easy task to extract roads from those maps. Many methods use user input to achieve this goal. In this paper we focus on an automated method to extract roads by using color segmentation and linear features detection to search for seed points having a high probability to belong to roads. Those seeds are then expanded before choosing to keep or to discard the extracted element.
KW - Raster map
KW - Road extraction
KW - Seed points
UR - http://www.scopus.com/inward/record.url?scp=84872540730&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872540730&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84872540730
SN - 9784901122115
T3 - Proceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011
SP - 243
EP - 246
BT - Proceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011
T2 - 12th IAPR Conference on Machine Vision Applications, MVA 2011
Y2 - 13 June 2011 through 15 June 2011
ER -