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@TECHREPORT{Scharr:154525,
      author       = {Scharr, Hanno and Minervini, Massimo and Fischbach, Andreas
                      and Tsaftaris, Sotirios A.},
      title        = {{A}nnotated {I}mage {D}atasets of {R}osette {P}lants},
      number       = {-},
      reportid     = {FZJ-2014-03837, -},
      pages        = {16 p.},
      year         = {2014},
      abstract     = {While image-based approaches to plant phenotyping are
                      gaining momentum, benchmark data focusing on typical imaging
                      situations and tasks in plant phenotyping are still lacking,
                      making it difficult to compare existing methodologies. This
                      report describes a benchmark dataset of raw and annotated
                      images of plants. We describe the plant material,
                      environmental conditions, and imaging setup and procedures,
                      as well as the datasets where this image selection stems
                      from. We also describe the annotation process, since all of
                      these images have been manually segmented by experts, such
                      that each leaf has its own label. Color images in the
                      dataset show top-down views on young rosette plants. Two
                      datasets show different genotypes of Arabidopsis while
                      another dataset shows tobacco (Nicoticana tobacum) under
                      different treatments. A version of the dataset, described
                      also in this report, is in the public domain at
                      http://www.plant-phenotyping.org/CVPPP2014-dataset and can
                      be used for the purpose of plant/leaf segmentation from
                      background, with accompanying evaluation scripts. This
                      version was used in the Leaf Segmentation Challenge (LSC) of
                      the Computer Vision Problems in Plant Phenotyping (CVPPP
                      2014) workshop organized in conjunction with the 13th
                      European Conference on Computer Vision (ECCV), in Zürich,
                      Switzerland. We hope with the release of this, and future,
                      dataset(s) to invigorate the study of computer vision
                      problems and the development of algorithms in the context of
                      plant phenotyping. We also aim to provide to the computer
                      vision community another interesting dataset on which new
                      algorithmic developments can be evaluated.},
      cin          = {IBG-2},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {89582 - Plant Science (POF2-89582) / GARNICS - Gardening
                      with a Cognitive System (247947) / DPPN - Deutsches Pflanzen
                      Phänotypisierungsnetzwerk (BMBF-031A053A)},
      pid          = {G:(DE-HGF)POF2-89582 / G:(EU-Grant)247947 /
                      G:(DE-Juel1)BMBF-031A053A},
      typ          = {PUB:(DE-HGF)29},
      url          = {https://juser.fz-juelich.de/record/154525},
}