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|Title:||Computing consensus curves|
|Authors:||De, La, Cruz, L.|
Shen, P. S.
|Citation:||Computing consensus curves / L. De La Cruz, S. Kobourov, S. Pupyrev, et al. — DOI 10.1007/978-3-319-07959-2_19 // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). — 2014. — Vol. 8504 LNCS. — P. 223-234.|
|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. © 2014 Springer International Publishing.|
|Appears in Collections:||Научные публикации, проиндексированные в SCOPUS и WoS CC|
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