Information
- Publication Type: Bachelor Thesis
- Workgroup(s)/Project(s):
- Date: January 2018
- Date (Start): August 2017
- Date (End): January 2018
- Matrikelnummer: 01426125
- First Supervisor: Viktor Vad
Abstract
Plant root phenotyping can be a tedious process if done manually, since it typically requires large data sets to be processed. The solution to this problem are automatic phenotyping pipelines, which allow significantly higher throughput than manual methods, by eliminating the need for human intervention. These pipelines rely on the robustness of automatic segmentation and detection methods for various plant characteristics. Due to numerous confounding factors, the detection of the hypocotyl/root transition point is still an unsolved task. In this thesis a novel approach to this problem, utilizing Statistical Break Point Analysis based on custom plant features, is presented. The approach has been developed using a visual analytics framework called PlateViewer, which was especially built for this task. The framework is able to analyze individual Arabidopsis Thaliana seedlings, taken from agar plate scan images, produced by an automatic phenotyping pipeline.Additional Files and Images
Weblinks
No further information available.BibTeX
@bachelorsthesis{Strohmayer-2018-BT,
title = "A Visual Analytics Approach to Hypocotyl/Root Transition
Detection in Arabidopsis Thaliana",
author = "Julian Strohmayer",
year = "2018",
abstract = "Plant root phenotyping can be a tedious process if done
manually, since it typically requires large data sets to be
processed. The solution to this problem are automatic
phenotyping pipelines, which allow significantly higher
throughput than manual methods, by eliminating the need for
human intervention. These pipelines rely on the robustness
of automatic segmentation and detection methods for various
plant characteristics. Due to numerous confounding factors,
the detection of the hypocotyl/root transition point is
still an unsolved task. In this thesis a novel approach to
this problem, utilizing Statistical Break Point Analysis
based on custom plant features, is presented. The approach
has been developed using a visual analytics framework called
PlateViewer, which was especially built for this task. The
framework is able to analyze individual Arabidopsis Thaliana
seedlings, taken from agar plate scan images, produced by an
automatic phenotyping pipeline. ",
month = jan,
address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
school = "Institute of Computer Graphics and Algorithms, Vienna
University of Technology ",
URL = "https://www.cg.tuwien.ac.at/research/publications/2018/Strohmayer-2018-BT/",
}