3-D Histogram-Based Segmentation and Leaf Detection for Rosette Plants

By Jean-Michel Pape, Christian Klukas. 2014

97 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Environmental Engineering, Plant Science

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Citations in the corpus

Recurrent Neural Networks for Semantic Instance Segmentation (2017, neutral): “We compare with participants to the CVPPP challenge [39, 42, 30, 15], the two recurrent models in [36, 34] and the metric learning based approach from [12].”
Recurrent Instance Segmentation (2015, background): “All code and models produced in this work will be made publicly available.”
Finely-grained annotated datasets for image-based plant phenotyping (2016, methodology): “…contests (see Section 4) using a subset of the annotated data presented here showed that the number of available images and criteria used allowed computer vision scientists to work successfully on these problems (see e.g. (Scharr et al., 2015; Pape and Klukas, 2014, 2015; Giu ﬀ rida et al., 2015)).”
Semantic Instance Segmentation with a Discriminative Loss Function (2017, neutral): “2 IPK [25] 74 .”
Learning to Count Leaves in Rosette Plants (2015, methodology): “For comparison the findings of Pape and Klukas [30] on the same testing set are shown (values for only two metrics were available).”
Utilizing machine learning approaches to improve the prediction of leaf counts and individual leaf segmentation of rosette plant images (2015, methodology): “Compared to the results of the LSC 2014 challenge [12] our new approach shows a better performance on all training data sets.”
Crop Phenomics: Current Status and Perspectives (2019, background): “However, the simple image processing pipeline is still very diﬃcult to handle with non-linear, non-geometric phenotyping tasks (Pape and Klukas, 2015).”
Leaf segmentation in plant phenotyping: a collation study (2015, methodology): “Algorithms and the papers were subject to peer review and the leading algorithm [43] presented at the CVPPP workshop.”
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks (2017, methodology): “Both results from the literature show signiﬁcantly degraded performance on a selection the three benchmark tasks—multi-accession Arabidopsis for Giuﬀrida et al. (2015), and both Col-0 Arabidopsis and tobacco for Pape and Klukas (2015).”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, methodology): “Algorithms and the papers were subject to peer review and the leading algorithm [43] presented at the CVPPP workshop.”
An interactive tool for semi-automated leaf annotation (2015, methodology): “Even when relying only on the simplest form of leaf annotation (dots), our approach outperforms the state-of-the-art automated leaf segmentation obtained by Pape and Klukas [27].”