Semi-automated Shoot Image Segmentation using K-means Clusering of Eigen-colors - kmSeg v0.1

Introduction

kmSeg tool was developed to enable efficient supervised segmentation of plant shoot images for subsequent training of machine and deep learning algorithms.

Key Features

The kmSeg tool automatically clusters image colors into a smalle predefined number of classes (typically between 10 and 25) that are in the next step manually assigned to either plant or non-plant (background) categories.

Downloads

download Windows x64 (247MB)
download Linux (283MB)
Quick Guide (3MB)

By downloading, installing and/or using this software the user agrees with the following license conditions.

This software requires MATLAB run time libraries (2018b) to be installed:

MATLAB run time for Windows x64 (~2GB).
MATLAB run time for Linux (~2GB).

Windows users should also install the run-time components from the following link: https://www.microsoft.com/en-us/download/details.aspx?id=48145.

Quick Start

For install notes and further informations, please see the Quick Guide.

References

[1] MH,EG Semi-automated Shoot Image Segmentation using K-means Clusering of Eigen-colors - kmSeg v0.1, (unpublished)


© 2019, Image Analysis Group, IPK Gatersleben Impressum
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)
OT Gatersleben
Corrensstrasse 3
06466 Stadt Seeland,
Telefon: +49 (0)39482 50
Telefax: +49 (0)39482 5139
E-Mail: info@ipk-gatersleben.de
Internet: www.ipk-gatersleben.de