%0 Journal Article
%A Dellen, B.
%A Scharr, Hanno
%A Torras, C.
%T Growth signature of rosette plants from time-lapse video
%J IEEE ACM transactions on computational biology and bioinformatics
%V PP
%N 99
%@ 1557-9964
%C New York, NY
%I IEEE
%M FZJ-2013-05546
%P 1-11
%D 2015
%X Plant growth is a dynamic process, and the precisecourse  of  events  during  early  plant  development  is  of  majorinterest  for  plant  research.  In  this  work,  we  investigate  thegrowth   of   rosette   plants   by   processing   time-lapse   videos   ofgrowing  plants,  where  we  use  Nicotiana  tabacum  (tobacco)  asa  model  plant.  In  each  frame  of  the  video  sequences,  potentialleaves  are  detected  using  a  leaf-shape  model.  These  detectionsare  prone  to  errors  due  to  the  complex  shape  of  plants  andtheir   changing   appearance   in   the   image,   depending   on   leafmovement,  leaf  growth,  and  illumination  conditions.  To  copewith  this  problem,  we  employ  a  novel  graph-based  trackingalgorithm  which  can  bridge  gaps  in  the  sequence  by  linkingleaf detections across a range of neighboring frames. We use theoverlap of fitted leaf models as a pairwise similarity measure, andforbid graph edges that would link leaf detections within a singleframe.  We  tested  the  method  on  a  set  of  tobacco-plant  growthsequences, and could track the first leaves of the plant, includingpartially or temporarily occluded ones, along complete sequences,demonstrating the applicability of the method to automatic plantgrowth analysis. All seedlings displayed approximately the samegrowth   behavior,   and   a   characteristic   growth   signature   wasfound.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:000368292400027
%R 10.1109/TCBB.2015.2404810
%U https://juser.fz-juelich.de/record/139562