Mind over machine: the power of human intuition and expertise in the era of the computer
Mind over machine: the power of human intuition and expertise in the era of the computer
Computing the minimum Hausdorff distance between two point sets on a line under translation
Information Processing Letters
Modeling and managing uncertainty in object-based geospatial information systems
Modeling and managing uncertainty in object-based geospatial information systems
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Assessing positional and modelling uncertainties in vector-based spatial processes and analyses in geographical information systems
Real-time GPS track simplification algorithm for outdoor navigation of visually impaired
Journal of Network and Computer Applications
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Automatic generalization is a process for representing geographical objects with different degrees of detail on a digital map. The positional error for each geographical object is propagated through the process and a generalization error is also introduced by the generalization. Previous research has focused mainly on measuring the generalization error. This paper presents an analytical model for assessing the positional error in the generalized object by considering both error propagation from the original data and the generalization error. The analytical model provides a shape dissimilarity value that indicates the shape difference between the original data with a positional error and its simplified version. This model is able to objectively and automatically determine the applicability of the generalized data for further applications to geographical information system (GIS) problems. It can also deal with a large amount of data in GIS. Therefore, the analytical model presented, which provides a more comprehensive shape measure for assessing positional error in data derived from the generalization, is valuable in the development of automatic generalization.