Computing Consensus Curves

  • Livio De La Cruz
  • Stephen Kobourov
  • Sergey Pupyrev
  • Paul S. Shen
  • Sankar Veeramoni
Conference paper

DOI: 10.1007/978-3-319-07959-2_19

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8504)
Cite this paper as:
De La Cruz L., Kobourov S., Pupyrev S., Shen P.S., Veeramoni S. (2014) Computing Consensus Curves. In: Gudmundsson J., Katajainen J. (eds) Experimental Algorithms. SEA 2014. Lecture Notes in Computer Science, vol 8504. Springer, Cham

Abstract

We study the problem of extracting accurate average ant trajectories from many (inaccurate) input trajectories contributed by citizen scientists. Although there are many generic software tools for motion tracking and specific ones for insect tracking, even untrained humans are better at this task. We consider several local (one ant at a time) and global (all ants together) methods. Our best performing algorithm uses a novel global method, based on finding edge-disjoint paths in a graph constructed from the input trajectories. The underlying optimization problem is a new and interesting network flow variant. Even though the problem is NP-complete, two heuristics work well in practice, outperforming all other approaches, including the best automated system.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Livio De La Cruz
    • 1
  • Stephen Kobourov
    • 1
  • Sergey Pupyrev
    • 1
    • 2
  • Paul S. Shen
    • 1
  • Sankar Veeramoni
    • 1
  1. 1.Department of Computer ScienceUniversity of ArizonaTucsonUSA
  2. 2.Institute of Mathematics and Computer ScienceUral Federal UniversityEkaterinburgRussia

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