Annotated Image Datasets of Rosette Plants

By Hanno Scharr, Massimo Minervini, Andreas Fischbach, Sotirios A. Tsaftaris. 2014

101 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Shape-aware Instance Segmentation (2016, background): “As such, it has many applications in diverse areas, such as autonomous driving [32], personal robotics [11] and plant analytics [27].”
Finely-grained annotated datasets for image-based plant phenotyping (2016, background): “For further details on treatments, environmental conditions, and acquisition times we refer to Scharr et al. (2014).”
Leaf segmentation in plant phenotyping: a collation study (2015, methodology): “We briefly describe the annotated dataset, the first of its kind, that was used to test and evaluate the methods for the segmentation of individual leaves in image-based plant phenotyping experiments (see Figure 1 and also [48]).”
Boundary-Aware Instance Segmentation (2016, background): “As such, it has many applications in diverse areas, such as autonomous driving [35], personal robotics [12] and plant analytics [30].”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, methodology): “1: Example images of Arabidopsis and tobacco from the datasets used in this study [48].”
Learning to Count Leaves in Rosette Plants (2015, methodology): “Image data: We use three datasets, namely A1, A2, and A3, consisting of images showing top views on individual plants provided by the LCC CVPPP 2015 challenge organizers [25, 32].”
An interactive tool for semi-automated leaf annotation (2015, methodology): “We adopt Arabidopsis image data from the ‘A1’ training set [30] and vary the number of available training data and keep the remainder of the training data as a validation test.”
Multi-modality imagery database for plant phenotyping (2016, methodology): “Recent progress in imaging sensor, robotics and automation technologies has recently led to the rapid development of highlyautomated, non-destructive, high-throughput methods for visual phenotyping [Furbank & Tester, 2011, Kramer et al., 2015].”
Utilizing machine learning approaches to improve the prediction of leaf counts and individual leaf segmentation of rosette plant images (2015, background): “The leading approach [12] showed inaccuracies in resolving leaf overlaps in the training and test data sets (data sets and approaches for data analysis are described in detail in [15] and [10]).”
3-D Histogram-Based Segmentation and Leaf Detection for Rosette Plants (2014, methodology): “Our methods development are related to a dataset provided through 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) [13].”