@inproceedings{Glowacki-CVPR2014,
  IS = { zkontrolovano 02 Jan 2015 },
UPDATE   = { 2014-12-19 },
author      = {Glowacki, Przemislaw and Am{\' a}vel Pinheiro, Miguel and
    Turetken, Engin and Sznitman, Raphael and Lebrecht, Daniel and
    Kybic, Jan and Hotlmaat, Anthony and Fua, Pascal},
affiliation = {NULL-13133-NULL-NULL-NULL-13133-NULL-NULL},
Title       = {{Reconstructing Evolving Tree Structures in Time Lapse Sequences}},
booktitle   = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages       = { 3035--3042 },
book_pages  = { 4304 },
editor      = {  },
isbn        = { 978-1-4799-5119-2 },
publisher   = { IEEE  Computer Society },
address     = { Los Alamitos, USA },
volume      = {  },
month       = { June },
day         = { 24--27 },
Year        = {2014},
venue       = { Columbus, US },
keywords    = {Delineation, Time Evolving, Dynamic Tree Reconstruction},
annote      = {We propose an approach to reconstructing tree structures
    that evolve over time in 2D images and 3D image stacks such as
    neuronal axons or plant branches. Instead of reconstructing
    structures in each image independently, we do so for all images
    simultaneously to take advantage of temporal-consistency constraints.
    We show that this problem can be formulated as a Quadratic Mixed
    Integer Program and solved efficiently. The outcome of our approach
    is a framework that provides substantial improvements in
    reconstructions over traditional single time-instance formulations.
    Furthermore, an added benefit of our approach is the ability to
    automatically detect places where significant changes have occurred
    over time, which is challenging when considering large amounts of
    data.},
Project     = {SGS12/190/OHK3/3T/13, GACR P202/11/0111, SFRH/BD/77134/2011},
Psurl       = {[Glowacki-CVPR2014.pdf]},
Url         = {ftp://cmp.felk.cvut.cz/pub/cmp/articles/amavemig/Glowacki-CVPR2014.pdf},
}