The crust and the &Bgr;-Skeleton: combinatorial curve reconstruction
Graphical Models and Image Processing
Curve reconstruction: connecting dots with good reason
SCG '99 Proceedings of the fifteenth annual symposium on Computational geometry
Road network reconstruction for organizing paths
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Reconstructing curves with sharp corners
Computational Geometry: Theory and Applications
Local homology transfer and stratification learning
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Constructing street networks from GPS trajectories
ESA'12 Proceedings of the 20th Annual European conference on Algorithms
On vehicle tracking data-based road network generation
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Probabilistic street-intersection reconstruction from GPS trajectories: approaches and challenges
Proceedings of the Third ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data
Hi-index | 0.00 |
Many real-world data sets can be viewed of as noisy samples of special types of metric spaces called metric graphs [16]. Building on the notions of correspondence and Gromov-Hausdorff distance in metric geometry, we describe a model for such data sets as an approximation of an underlying metric graph. We present a novel algorithm that takes as an input such a data set, and outputs the underlying metric graph with guarantees. We also implement the algorithm, and evaluate its performance on a variety of real world data sets.