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handle: 2117/17768
We investigate the concept of a median among a set of trajectories. We establish criteria that a “median trajectory” should meet, and present two different methods to construct a median for a set of input trajectories. The first method is very simple, while the second method is more complicated and uses homotopy with respect to sufficiently large faces in the arrangement formed by the trajectories. We give algorithms for both methods, analyze the worst-case running time, and show that under certain assumptions both methods can be implemented efficiently. We empirically compare the output of both methods on randomly generated trajectories, and evaluate whether the two methods yield medians that are according to our intuition. Our results suggest that the second method, using homotopy, performs considerably better. Peer Reviewed
geometric algorithms, Applied Mathematics, homotopy, CG, TRAJ, GIS, Geometria computacional, Computer Science Applications, Trajectories, Àrees temàtiques de la UPC::Matemàtiques i estadística::Geometria::Geometria computacional, Computer graphics; computational geometry (digital and algorithmic aspects), trajectories, Geometric algorithms, :Matemàtiques i estadística::Geometria::Geometria computacional [Àrees temàtiques de la UPC], Homotopy, Computer Science(all)
geometric algorithms, Applied Mathematics, homotopy, CG, TRAJ, GIS, Geometria computacional, Computer Science Applications, Trajectories, Àrees temàtiques de la UPC::Matemàtiques i estadística::Geometria::Geometria computacional, Computer graphics; computational geometry (digital and algorithmic aspects), trajectories, Geometric algorithms, :Matemàtiques i estadística::Geometria::Geometria computacional [Àrees temàtiques de la UPC], Homotopy, Computer Science(all)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 46 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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