Segmenting color images into surface patches by exploiting sparse depth data
By Babette K. Dellen, Guillem Alenyà, Sergi Foix, Carme Torras. 2011
18 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Environmental Engineering, Plant Science, 3D Vision & Pose
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
- Massimo Minervini
- Sotirios A. Tsaftaris
- Hanno Scharr
- Andreas Fischbach
- Christian Klukas
- Jean-Michel Pape
- Mario Valerio Giuffrida
- Ian Stavness
In Environmental Engineering
- Hanno Scharr
- Sotirios A. Tsaftaris
- Massimo Minervini
- Andreas Fischbach
- Ian Stavness
- Jordan R. Ubbens
- Richard S. Zemel
- Mengye Ren
In Plant Science
- Massimo Minervini
- Sotirios A. Tsaftaris
- Andreas Fischbach
- Hanno Scharr
- Ian Stavness
- Jordan R. Ubbens
- Christian Klukas
- Jean-Michel Pape
In 3D Vision & Pose
Citations in the corpus
- Finely-grained annotated datasets for image-based plant phenotyping (2016, background): “Additional depth information as can be computed from a pair of images with di ff erent focus (Favaro and Soatto, 2007) of the Arabidopsis dataset or stereo images (Biskup et al., 2007) of the Tobacco dataset, to be released in the future, may further facilitate segmentation (Dellen et al., 2011).”
- Annotated Image Datasets of Rosette Plants (2014, background): “Additional depth information as can be computed from stereo images [35] for the tobacco dataset, to be released in the future, may further facilitate segmentation [36, 37, 38, 39].”