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@ARTICLE{Scharr:280946,
      author       = {Scharr, Hanno and Minervini, Massimo and French, Andrew P.
                      and Klukas, Christian and Kramer, David M. and Liu, Xiaoming
                      and Luengo, Imanol and Pape, Jean-Michel and Polder, Gerrit
                      and Vukadinovic, Danijela and Yin, Xi and Tsaftaris,
                      Sotirios A.},
      title        = {{L}eaf segmentation in plant phenotyping: a collation
                      study},
      journal      = {Machine vision and applications},
      volume       = {27},
      number       = {4},
      issn         = {1432-1769},
      address      = {Berlin},
      publisher    = {Springer},
      reportid     = {FZJ-2016-00657},
      pages        = {585-606},
      year         = {2016},
      abstract     = {Image-based plant phenotyping is a growing application area
                      of computer vision in agriculture. A key task is the
                      segmentation of all individual leaves in images. Here we
                      focus on the most common rosette model plants, Arabidopsis
                      and young tobacco. Although leaves do share appearance and
                      shape characteristics, the presence of occlusions and
                      variability in leaf shape and pose, as well as imaging
                      conditions, render this problem challenging. The aim of this
                      paper is to compare several leaf segmentation solutions on a
                      unique and first-of-its-kind dataset containing images from
                      typical phenotyping experiments. In particular, we report
                      and discuss methods and findings of a collection of
                      submissions for the first Leaf Segmentation Challenge of the
                      Computer Vision Problems in Plant Phenotyping workshop in
                      2014. Four methods are presented: three segment leaves by
                      processing the distance transform in an unsupervised
                      fashion, and the other via optimal template selection and
                      Chamfer matching. Overall, we find that although separating
                      plant from background can be accomplished with satisfactory
                      accuracy (>90 $\%$ Dice score), individual leaf segmentation
                      and counting remain challenging when leaves overlap.
                      Additionally, accuracy is lower for younger leaves. We find
                      also that variability in datasets does affect outcomes. Our
                      findings motivate further investigations and development of
                      specialized algorithms for this particular application, and
                      that challenges of this form are ideally suited for
                      advancing the state of the art. Data are publicly available
                      (online at
                      http://​www.​plant-phenotyping.​org/​datasets) to
                      support future challenges beyond segmentation within this
                      application domain.},
      cin          = {IBG-2},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582) / GARNICS - Gardening with a
                      Cognitive System (247947) / 583 - Innovative Synergisms
                      (POF3-583)},
      pid          = {G:(DE-HGF)POF3-582 / G:(EU-Grant)247947 /
                      G:(DE-HGF)POF3-583},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000375611700012},
      doi          = {10.1007/s00138-015-0737-3},
      url          = {https://juser.fz-juelich.de/record/280946},
}