Rosette Tracker: An Open Source Image Analysis Tool for Automatic Quantification of Genotype Effects1[C][W]

By Jonas De Vylder, Filip Vandenbussche, Yuming Hu, Wilfried Philips, D. Van Der Straeten. 2012

141 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Ecology, Plant Science, Computational Biology & Bioinformatics

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Citations in the corpus

Finely-grained annotated datasets for image-based plant phenotyping (2016, methodology): “In simple cases this can be solved by color thresholding and other unsupervised segmentation approaches (Walter et al., 2007; De Vylder et al., 2012), but when scene complexity is high (non-smooth background, non-uniform lighting, plant overlap, presence of moss on soil) sophisticated…”
A Review of Imaging Techniques for Plant Phenotyping (2014, methodology): “[97] used color segmentation based on phenotype parameters extracted from RGB space including the shoot biomass (to quantify the image-based projected area), diameter (the”
An interactive tool for semi-automated leaf annotation (2015, methodology): “In plant applications most annotation tools are limited towards annotating whole plants (hence, not individual leaves) [9, 16, 24], leaves in isolation (e.”
Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis[W][OPEN] (2014, methodology): “The number of traits, including image-based features and model-derived parameters, extracted from our pipeline greatly exceeds existing pipelines (Wang et al., 2009; Hartmann et al., 2011; De Vylder et al., 2012; Green et al., 2012; Paproki et al., 2012; Zhang et al., 2012; Camargo et al., 2014).”
3-D Histogram-Based Segmentation and Leaf Detection for Rosette Plants (2014, background): “There are a various number of applications which support fully automated or semi-automated plant image analysis, especially for rosette plants, as described in [1],[2],[3],[4],[5] and [6].”
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks (2017, background): “Non-linear, geometric phenotypic features, such as compactness and diameter of rosettes (De Vylder et al., 2012), can be measured as a straight-forward processing step after plant segmentation.”
Leaf segmentation in plant phenotyping: a collation study (2015, background): “Several authors have dealt with the segmentation of a live plant from background to measure growth using unsupervised [17,25] and semi-supervised methods [36], but not of individual leaves.”
Annotated Image Datasets of Rosette Plants (2014, background): “Several laboratories developed customized image processing pipelines to analyze the image data acquired during experiments [2, 4, 5, 6].”
Image-based plant phenotyping with incremental learning and active contours (2014, methodology): “On the other hand, some approaches assume static cameras with a fixed field of view (De Vylder et al., 2012; Hartmann et al., 2011; Kokorian et al., 2010; Tsaftaris and Noutsos, 2009) and are largely used in growth-chamber experiments of small plant models such as Arabidopsis.”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, background): “Several authors have dealt with the segmentation of a live plant from background to measure growth using unsupervised [17,25] and semi-supervised methods [36], but not of individual leaves.”