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dc.contributor.authorGlowacki, P.
dc.contributor.authorPinheiro, M. A.
dc.contributor.authorMosinska, A.
dc.contributor.authorTuretken, E.
dc.contributor.authorLebrecht, D.
dc.contributor.authorSznitman, R.
dc.contributor.authoret al.
dc.date.accessioned2021-12-09T14:00:20Z
dc.date.available2021-12-09T14:00:20Z
dc.date.issued2018
dc.identifier.citationIeee Transactions on Pattern Analysis and Machine Intelligence, vol. 40 (3), pp. 755-761, Mar 2018.
dc.identifier.urihttps://yoda.csem.ch/handle/20.500.12839/220
dc.description.abstractWe propose a novel approach to reconstructing curvilinear tree structures evolving over time, such as road networks in 20 aerial images or neural structures in 3D microscopy stacks acquired in vivo. To enforce temporal consistency, we simultaneously process all images in a sequence, as opposed to reconstructing structures of interest in each image independently. We formulate the problem as a Quadratic Mixed Integer Program and demonstrate the additional robustness that comes from using all available visual clues at once, instead of working frame by frame. Furthermore, when the linear structures undergo local changes over time, our approach automatically detects them.
dc.subjectCurvilinear networks, tubular structures, curvilinear structures, automated reconstruction, temporal consistency, integer programming, Computer Science, Engineering
dc.titleReconstructing Evolving Tree Structures in Time Lapse Sequences by Enforcing Time-Consistency
dc.typeJournal Article
dc.type.csemdivisionsDiv-M
dc.type.csemresearchareasData & AI
dc.identifier.doihttps://doi.org/10.1109/tpami.2017.2680444


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