Title :
Raster Map Image Analysis
Author :
Henderson, Thomas C. ; Linton, Trevor
Author_Institution :
Sch. of Comput., Univ. of Utah, Salt Lake City, UT, USA
Abstract :
Raster map images (e.g., USGS) provide much information in digital form; however, the color assignments and pixel labels leave many serious ambiguities. A color histogram classification scheme is described, followed by the application of a tensor voting method to classify linear features in the map as well as intersections in linear feature networks. The major result is an excellent segmentation of roads, and road intersections are detected with about 93% recall and 66 % precision.
Keywords :
feature extraction; image classification; image colour analysis; color histogram classification; image segmentation; linear feature network; raster map image analysis; road intersection; tensor voting method; Cities and towns; Histograms; Image analysis; Image color analysis; Information analysis; Performance analysis; Rivers; Roads; Tensile stress; Voting; Map analysis; road intersection detection; road segmentation;
Conference_Titel :
Document Analysis and Recognition, 2009. ICDAR '09. 10th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-4500-4
Electronic_ISBN :
1520-5363
DOI :
10.1109/ICDAR.2009.31
