TY - GEN
T1 - A method for automatically extracting road layers from raster maps
AU - Chiang, Yao Yi
AU - Knoblock, Craig A.
PY - 2009
Y1 - 2009
N2 - To exploit the road network in raster maps, the first step is to extract the pixels that constitute the roads and then vectorize the road pixels. Identifying colors that represent roads in raster maps for extracting road pixels is difficult since raster maps often contain numerous colors due to the noise introduced during the processes of image compression and scanning. In this paper, we present an approach that minimizes the required user input for identifying the road colors representing the road network in a raster map. We can then use the identified road colors to extract road pixels from the map. Our approach can be used on scanned and compressed maps that are otherwise difficult to process automatically and tedious to process manually. We tested our approach with 100 maps from a variety of sources, which include 90 scanned maps with various compression levels and 10 computer generated maps. We successfully identified the road colors and extracted the road pixels from all test maps with fewer than four user labels per map on average.
AB - To exploit the road network in raster maps, the first step is to extract the pixels that constitute the roads and then vectorize the road pixels. Identifying colors that represent roads in raster maps for extracting road pixels is difficult since raster maps often contain numerous colors due to the noise introduced during the processes of image compression and scanning. In this paper, we present an approach that minimizes the required user input for identifying the road colors representing the road network in a raster map. We can then use the identified road colors to extract road pixels from the map. Our approach can be used on scanned and compressed maps that are otherwise difficult to process automatically and tedious to process manually. We tested our approach with 100 maps from a variety of sources, which include 90 scanned maps with various compression levels and 10 computer generated maps. We successfully identified the road colors and extracted the road pixels from all test maps with fewer than four user labels per map on average.
UR - http://www.scopus.com/inward/record.url?scp=71249117369&partnerID=8YFLogxK
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U2 - 10.1109/ICDAR.2009.274
DO - 10.1109/ICDAR.2009.274
M3 - Conference contribution
AN - SCOPUS:71249117369
SN - 9780769537252
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 838
EP - 842
BT - ICDAR2009 - 10th International Conference on Document Analysis and Recognition
T2 - ICDAR2009 - 10th International Conference on Document Analysis and Recognition
Y2 - 26 July 2009 through 29 July 2009
ER -