Show simple item record

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.identifier.citationIeee Transactions on Pattern Analysis and Machine Intelligence, vol. 40 (3), pp. 755-761, Mar 2018.
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.csemresearchareasData & AI

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

  • Research Publications
    The “Research Publications” collection provides bibliographic information for scientific papers including conference proceedings and presentations.

Show simple item record