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Algorithmica
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zbMATH Open
Article . 2013
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https://doi.org/10.1007/978-3-...
Part of book or chapter of book . 2010 . Peer-reviewed
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Algorithmica
Article . 2012 . Peer-reviewed
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DBLP
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Data sources: DBLP
Algorithmica
Article . 2013
Pure Utrecht University
Conference object . 2010
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Median Trajectories

Median trajectories
Authors: Kevin Buchin; Maike Buchin; Marc J. van Kreveld; Maarten Löffler; Rodrigo I. Silveira; Carola Wenk; Lionov Wiratma;

Median Trajectories

Abstract

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

Countries
Spain, Spain, Netherlands
Keywords

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)

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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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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46
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Top 10%
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63
43
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