Multi-leaf tracking from fluorescence plant videos

By Xi Yin, Xiaoming Liu, Jin Chen, David M. Kramer. 2014

53 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Plant Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Plant Science

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].”
Finely-grained annotated datasets for image-based plant phenotyping (2016, methodology): “…al., 2009; Nagel et al., 2012) together with customized image processing pipelines to analyze aspects of the acquired image data (Au-gustin et al., 2015; De Vylder et al., 2012; Hartmann et al., 2011; van der Heijden et al., 2012; Walter et al., 2007; Yin et al., 2014; M¨uller-Linow et al., 2015).”
An interactive tool for semi-automated leaf annotation (2015, background): “To this day, with some exceptions [27, 33], most leaf level analysis is performed manually.”
Learning to Count Leaves in Rosette Plants (2015, background): “In [42], leaf segmentation and tracking is performed in a fluorescence video sequence of growing Arabidopsis.”
Leaf segmentation in plant phenotyping: a collation study (2015, methodology): “We apply a multi-leaf tracking procedure [61] to transform each template, i.”
Utilizing machine learning approaches to improve the prediction of leaf counts and individual leaf segmentation of rosette plant images (2015, methodology): “Known approaches use time-lapse image data, 3-D information as input or images are acquired under well-defined conditions [1, 9, 18].”
Multi-modality imagery database for plant phenotyping (2016, methodology): “Note that [Yin et al., 2014a,Yin et al., 2014b] is designed for rosette plants like Arabidopsis.”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, methodology): “The former can be used for better leaf segmentation, e.g. via joint segmentation-tracking approaches [60,61].”
End-to-End Instance Segmentation with Recurrent Attention (2016, unknown): “RIS+CRF [35] 66.6 (8.7) 1.1 (0.9) MSU [36] 66.7 (7.6) 2.3 (1.6) Nottingham [36] 68.3 (6.3) 3.8 (2.0) Wageningen [43] 71.1 (6.2) 2.2 (1.6) IPK [30] 74.4 Evaluation metrics .”