Semantic feature analysis in raster maps

The extraction of semantic features from images of geographic maps is a difficult and interesting problem. Such features may be robustly segmented through the use of Gestalt principles such as similarity and continuity as realized through the use of tensor voting methods and color histogram analysis, respectively. A framework is developed implementing these Gestalt principles through various algorithms. Linear feature segmentation and intersection detection methods are given, and their performance is demonstrated on a set of real and synthetic map images.