MAGELLAN: Map Acquisition of GEographic Labels by Legend ANalysis |
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Authors: | Hanan Samet Aya Soffer |
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Affiliation: | (1) Computer Science Department, Center for Automation Research, Institute for Advanced Computer Science, University of Maryland at College Park, College Park, MD 20742, USA; e-mail: {hjs,aya}@umiacs.umd.edu--> , US |
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Abstract: | A system named MAGELLAN (denoting Map Acquisition of GEographic Labels by Legend ANalysis) is described that utilizes the
symbolic knowledge found in the legend of the map to drive geographic symbol (or label) recognition. MAGELLAN first scans
the geographic symbol layer(s) of the map. The legend of the map is located and segmented. The geographic symbols (i.e., labels)
are identified, and their semantic meaning is attached. An initial training set library is constructed based on this information.
The training set library is subsequently used to classify geographic symbols in input maps using statistical pattern recognition.
User interaction is required at first to assist in constructing the training set library to account for variability in the
symbols. The training set library is built dynamically by entering only instances that add information to it. MAGELLAN then
proceeds to identify the geographic symbols in the input maps automatically. MAGELLAN can be fine-tuned by the user to suit
specific needs. Recognition rates of over 93% were achieved in an experimental study on a large amount of data.
Received January 5, 1998 / Revised March 18, 1998 |
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Keywords: | :Map recognition – Document analysis – Object recognition – Image databases – Geographic information systems (GIS) |
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