Perceptual grouping for symbol chain tracking in digitized topographic maps

In this paper a new algorithm that applies perceptual grouping to detect and track discontinuous chains of symbols in digitized maps is proposed. The procedure is based on an artificial intelligence kernel that supervises three different auxiliary processes: the Search Strategy Generation module that is responsible for the strategy to scan pixels; the Symbol Detection (SD) module that extracts the recognized symbols; the Cost Function Evaluation (CFE) module that assigns a global quality index to each symbol by considering the whole course of the line. Selected Gestalt rules are used to optimize the grouping procedures. After the algorithm discussion, the problem of the extraction of dotted and dashed lines from digitized topographic maps is discussed. Experimental results on many maps of the Istituto Geografico Militare Italiano (IGMI) show a very good behavior: 92% of the discontinuous lines have been correctly chained, and the percentage of incorrectly classified symbols is also very small. (C) 1999 Elsevier Science B.V. All rights reserved.