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  3. Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC
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dc.contributor.authorDe, La, Cruz, L.en
dc.contributor.authorKobourov, S.en
dc.contributor.authorPupyrev, S.en
dc.contributor.authorShen, P. S.en
dc.contributor.authorVeeramoni, S.en
dc.date.accessioned2021-08-31T15:03:41Z-
dc.date.available2021-08-31T15:03:41Z-
dc.date.issued2014-
dc.identifier.citationComputing 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.en
dc.identifier.isbn9783319079585-
dc.identifier.issn3029743-
dc.identifier.otherFinal2
dc.identifier.otherAll Open Access, Green3
dc.identifier.otherhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84903692950&doi=10.1007%2f978-3-319-07959-2_19&partnerID=40&md5=6483818e823db4536b215d36e4f0f471
dc.identifier.otherhttp://arxiv.org/pdf/1212.0935.pdfm
dc.identifier.urihttp://elar.urfu.ru/handle/10995/102451-
dc.description.abstractWe 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.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherSpringer Verlagen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceLect. Notes Comput. Sci.2
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.subjectAUTOMATIONen
dc.subjectOPTIMIZATIONen
dc.subjectAUTOMATED SYSTEMSen
dc.subjectEDGE-DISJOINT PATHSen
dc.subjectGENERIC SOFTWARESen
dc.subjectGLOBAL METHODSen
dc.subjectINPUT TRAJECTORYen
dc.subjectMOTION TRACKINGen
dc.subjectNETWORK FLOWSen
dc.subjectOPTIMIZATION PROBLEMSen
dc.subjectTRAJECTORIESen
dc.titleComputing consensus curvesen
dc.typeConference Paperen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.1007/978-3-319-07959-2_19-
dc.identifier.scopus84903692950-
local.contributor.employeeDe La Cruz, L., Department of Computer Science, University of Arizona, Tucson, AZ, United States
local.contributor.employeeKobourov, S., Department of Computer Science, University of Arizona, Tucson, AZ, United States
local.contributor.employeePupyrev, S., Department of Computer Science, University of Arizona, Tucson, AZ, United States, Institute of Mathematics and Computer Science, Ural Federal University, Ekaterinburg, Russian Federation
local.contributor.employeeShen, P.S., Department of Computer Science, University of Arizona, Tucson, AZ, United States
local.contributor.employeeVeeramoni, S., Department of Computer Science, University of Arizona, Tucson, AZ, United States
local.description.firstpage223-
local.description.lastpage234-
local.volume8504 LNCS-
local.contributor.departmentDepartment of Computer Science, University of Arizona, Tucson, AZ, United States
local.contributor.departmentInstitute of Mathematics and Computer Science, Ural Federal University, Ekaterinburg, Russian Federation
local.identifier.pure370643-
local.identifier.pure552529e1-d3e3-4521-a206-856e38e0e294uuid
local.identifier.eid2-s2.0-84903692950-
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