Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
Boundary simplification in cartography preserving the characteristics of the shape features
Computers & Geosciences
Controlled Line Smoothing by Snakes
Geoinformatica
Data reduction of large vector graphics
Pattern Recognition
Ontology-driven map generalization
Journal of Visual Languages and Computing
An evaluation of the sinuosity effect on visualization of RDP simplified maps: an empirical study
Proceedings of the 2010 ACM Symposium on Applied Computing
A perception based selection of vector map LoDs for progressive transmission
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoStreaming
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In line generalization, results depend very much on the characteristics of the line. For this reason it would be useful to obtain an automatic segmentation and enrichment of lines in order to apply to each section the best algorithm and the appropriate parameter. In this paper we present a methodology for applying a line-classifying backpropagation artificial neural network (BANN) for a line segmentation task. The procedure is based on the use of a moving window along the line to detect changes in the sinuosity and directionality of the line. A summary of the BANN design is presented, and a test is performed over a set of roads from a 1:25k scale map with a recommendation of the value of the parameters of the moving window. Segmentation results were assessed by an independent group of experts; a summary of the evaluation procedure is shown.