Quantifying Barley Morphology Using the Euler Characteristic Transform
Abstract
Shape is foundational to biology. Observing and documenting shape has fueled biological understanding, and from this perspective, it is also a type of data. The vision of topological data analysis, that data is shape and shape is data, will be relevant as biology transitions into a data-driven era where meaningful interpretation of large data sets is a limiting factor. We focus first on quantifying the morphology of barley spikes and seeds using topological descriptors based on the Euler characteristic. We then successfully train a support vector machine to classify 28 different varieties of barley based solely on the shape of their grains.
Cite
Text
Amezquita et al. "Quantifying Barley Morphology Using the Euler Characteristic Transform." NeurIPS 2020 Workshops: TDA_and_Beyond, 2020.Markdown
[Amezquita et al. "Quantifying Barley Morphology Using the Euler Characteristic Transform." NeurIPS 2020 Workshops: TDA_and_Beyond, 2020.](https://mlanthology.org/neuripsw/2020/amezquita2020neuripsw-quantifying/)BibTeX
@inproceedings{amezquita2020neuripsw-quantifying,
title = {{Quantifying Barley Morphology Using the Euler Characteristic Transform}},
author = {Amezquita, Erik J and Quigley, Michelle and Ophelders, Tim and Landis, Jacob and Munch, Elizabeth and Chitwood, Daniel and Koenig, Daniel},
booktitle = {NeurIPS 2020 Workshops: TDA_and_Beyond},
year = {2020},
url = {https://mlanthology.org/neuripsw/2020/amezquita2020neuripsw-quantifying/}
}