A two-step registration-classification approach to automated segmentation of multimodal images for high-throughput greenhouse plant phenotyping

Background Automated segmentation of large amount of image data is one of the major bottlenecks in high-throughput plant phenotyping. Dynamic optical appearance of developing plants, inhomogeneous scene illumination, shadows and reflections in plant and background regions complicate automated segmen...

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Vydané v:	Plant methods Ročník 16; číslo 1; s. 95
Hlavní autori:	Henke, Michael, Junker, Astrid, Neumann, Kerstin, Altmann, Thomas, Gladilin, Evgeny
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 09.07.2020 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Algorithms Automation Biological Techniques Biomedical and Life Sciences Blurring Cameras Classification color Deep learning Developmental stages Flowers & plants Fluorescence Fluorescence imaging Greenhouse plant phenotyping Greenhouses Ground truth Identification and classification Image acquisition Image analysis Image classification Image detection Image processing Image segmentation Life Sciences lighting Machine learning Methodology Methods Multimodal image alignment phenotype Phenotyping Physiological aspects Plant Sciences Plants (Organisms) Principal components analysis Registration Relocation Supervised image segmentation Visible light imaging Germany Aachen Germany Visible light imaging Fluorescence imaging Supervised image segmentation Multimodal image alignment Greenhouse plant phenotyping Machine learning
ISSN:	1746-4811, 1746-4811
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