Map Symbol Recognition Using Directed Hausdorff Distance and a Neural Network Classifier †
نویسندگان
چکیده
A method for map symbol recognition is presented in this paper. Our objective in developing this recognition method is to make recognition efficient, robust and near perfect for handling very large maps with many symbols of different scales and orientations. Our method first utilizes the directed Hausdorff distance as a measure of similarity for selecting possible candidates of user defined models of symbols. This selection will collect as many candidates as possible, in order not to miss any symbols. Neural networks are then utilized to eliminate the false positives among those candidates. Implementation details and experiment results are presented.
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