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Deep Segmentation of Point Clouds of Wheat

The 3D analysis of plants has become increasingly effective in modeling the relative structure of organs and other traits of interest. In this paper, we introduce a novel pattern-based deep neural network, Pattern-Net, for segmentation of point clouds of wheat. This study is the first to segment the...

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Autores principales: Ghahremani, Morteza, Williams, Kevin, Corke, Fiona M. K., Tiddeman, Bernard, Liu, Yonghuai, Doonan, John H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8025700/
https://www.ncbi.nlm.nih.gov/pubmed/33841454
http://dx.doi.org/10.3389/fpls.2021.608732
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author Ghahremani, Morteza
Williams, Kevin
Corke, Fiona M. K.
Tiddeman, Bernard
Liu, Yonghuai
Doonan, John H.
author_facet Ghahremani, Morteza
Williams, Kevin
Corke, Fiona M. K.
Tiddeman, Bernard
Liu, Yonghuai
Doonan, John H.
author_sort Ghahremani, Morteza
collection PubMed
description The 3D analysis of plants has become increasingly effective in modeling the relative structure of organs and other traits of interest. In this paper, we introduce a novel pattern-based deep neural network, Pattern-Net, for segmentation of point clouds of wheat. This study is the first to segment the point clouds of wheat into defined organs and to analyse their traits directly in 3D space. Point clouds have no regular grid and thus their segmentation is challenging. Pattern-Net creates a dynamic link among neighbors to seek stable patterns from a 3D point set across several levels of abstraction using the K-nearest neighbor algorithm. To this end, different layers are connected to each other to create complex patterns from the simple ones, strengthen dynamic link propagation, alleviate the vanishing-gradient problem, encourage link reuse and substantially reduce the number of parameters. The proposed deep network is capable of analysing and decomposing unstructured complex point clouds into semantically meaningful parts. Experiments on a wheat dataset verify the effectiveness of our approach for segmentation of wheat in 3D space.
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spelling pubmed-80257002021-04-08 Deep Segmentation of Point Clouds of Wheat Ghahremani, Morteza Williams, Kevin Corke, Fiona M. K. Tiddeman, Bernard Liu, Yonghuai Doonan, John H. Front Plant Sci Plant Science The 3D analysis of plants has become increasingly effective in modeling the relative structure of organs and other traits of interest. In this paper, we introduce a novel pattern-based deep neural network, Pattern-Net, for segmentation of point clouds of wheat. This study is the first to segment the point clouds of wheat into defined organs and to analyse their traits directly in 3D space. Point clouds have no regular grid and thus their segmentation is challenging. Pattern-Net creates a dynamic link among neighbors to seek stable patterns from a 3D point set across several levels of abstraction using the K-nearest neighbor algorithm. To this end, different layers are connected to each other to create complex patterns from the simple ones, strengthen dynamic link propagation, alleviate the vanishing-gradient problem, encourage link reuse and substantially reduce the number of parameters. The proposed deep network is capable of analysing and decomposing unstructured complex point clouds into semantically meaningful parts. Experiments on a wheat dataset verify the effectiveness of our approach for segmentation of wheat in 3D space. Frontiers Media S.A. 2021-03-24 /pmc/articles/PMC8025700/ /pubmed/33841454 http://dx.doi.org/10.3389/fpls.2021.608732 Text en Copyright © 2021 Ghahremani, Williams, Corke, Tiddeman, Liu and Doonan. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Ghahremani, Morteza
Williams, Kevin
Corke, Fiona M. K.
Tiddeman, Bernard
Liu, Yonghuai
Doonan, John H.
Deep Segmentation of Point Clouds of Wheat
title Deep Segmentation of Point Clouds of Wheat
title_full Deep Segmentation of Point Clouds of Wheat
title_fullStr Deep Segmentation of Point Clouds of Wheat
title_full_unstemmed Deep Segmentation of Point Clouds of Wheat
title_short Deep Segmentation of Point Clouds of Wheat
title_sort deep segmentation of point clouds of wheat
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8025700/
https://www.ncbi.nlm.nih.gov/pubmed/33841454
http://dx.doi.org/10.3389/fpls.2021.608732
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