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Automated interpretation of 3D laserscanned point clouds for plant organ segmentation

Overview of attention for article published in BMC Bioinformatics, August 2015
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5 tweeters

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59 Dimensions

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84 Mendeley
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Title
Automated interpretation of 3D laserscanned point clouds for plant organ segmentation
Published in
BMC Bioinformatics, August 2015
DOI 10.1186/s12859-015-0665-2
Pubmed ID
Authors

Mirwaes Wahabzada, Stefan Paulus, Kristian Kersting, Anne-Katrin Mahlein

Abstract

Plant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation. Automated solutions are required to increase the efficiency of recent high-throughput plant phenotyping pipelines. However, plant geometrical properties vary with time, among observation scales and different plant types. The main objective of the present research is to develop a fully automated, fast and reliable data driven approach for plant organ segmentation. The automated segmentation of plant organs using unsupervised, clustering methods is crucial in cases where the goal is to get fast insights into the data or no labeled data is available or costly to achieve. For this we propose and compare data driven approaches that are easy-to-realize and make the use of standard algorithms possible. Since normalized histograms, acquired from 3D point clouds, can be seen as samples from a probability simplex, we propose to map the data from the simplex space into Euclidean space using Aitchisons log ratio transformation, or into the positive quadrant of the unit sphere using square root transformation. This, in turn, paves the way to a wide range of commonly used analysis techniques that are based on measuring the similarities between data points using Euclidean distance. We investigate the performance of the resulting approaches in the practical context of grouping 3D point clouds and demonstrate empirically that they lead to clustering results with high accuracy for monocotyledonous and dicotyledonous plant species with diverse shoot architecture. An automated segmentation of 3D point clouds is demonstrated in the present work. Within seconds first insights into plant data can be deviated - even from non-labelled data. This approach is applicable to different plant species with high accuracy. The analysis cascade can be implemented in future high-throughput phenotyping scenarios and will support the evaluation of the performance of different plant genotypes exposed to stress or in different environmental scenarios.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
France 1 1%
Australia 1 1%
Brazil 1 1%
Spain 1 1%
Japan 1 1%
Unknown 78 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 27%
Student > Ph. D. Student 18 21%
Student > Master 13 15%
Student > Bachelor 8 10%
Student > Doctoral Student 3 4%
Other 10 12%
Unknown 9 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 29%
Computer Science 21 25%
Engineering 14 17%
Biochemistry, Genetics and Molecular Biology 3 4%
Environmental Science 3 4%
Other 8 10%
Unknown 11 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 February 2016.
All research outputs
#5,894,701
of 10,444,782 outputs
Outputs from BMC Bioinformatics
#2,767
of 4,169 outputs
Outputs of similar age
#114,936
of 234,865 outputs
Outputs of similar age from BMC Bioinformatics
#71
of 114 outputs
Altmetric has tracked 10,444,782 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,169 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 234,865 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 114 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.