imageseg: Deep Learning Models for Image Segmentation
A general-purpose workflow for image segmentation using TensorFlow models based on the U-Net architecture by Ronneberger et al. (2015) <doi:10.48550/arXiv.1505.04597> and the U-Net++ architecture by Zhou et al. (2018) <doi:10.48550/arXiv.1807.10165>. We provide pre-trained models for assessing canopy density and understory vegetation density from vegetation photos. In addition, the package provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on grayscale or color images, both for binary and multi-class image segmentation.
| Version: |
0.5.0 |
| Imports: |
grDevices, keras, magick, magrittr, methods, purrr, stats, tibble, foreach, parallel, doParallel, dplyr |
| Suggests: |
R.rsp, testthat |
| Published: |
2022-05-29 |
| DOI: |
10.32614/CRAN.package.imageseg |
| Author: |
Juergen Niedballa 
[aut, cre],
Jan Axtner [aut],
Leibniz Institute for Zoo and Wildlife Research [cph] |
| Maintainer: |
Juergen Niedballa <niedballa at izw-berlin.de> |
| BugReports: |
https://github.com/EcoDynIZW/imageseg/issues |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
imageseg results |
Documentation:
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