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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 |
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 |
Reference manual: | imageseg.pdf |
Vignettes: |
A sample session in imageseg |
Package source: | imageseg_0.5.0.tar.gz |
Windows binaries: | r-devel: imageseg_0.5.0.zip, r-release: imageseg_0.5.0.zip, r-oldrel: imageseg_0.5.0.zip |
macOS binaries: | r-release (arm64): imageseg_0.5.0.tgz, r-oldrel (arm64): imageseg_0.5.0.tgz, r-release (x86_64): imageseg_0.5.0.tgz, r-oldrel (x86_64): imageseg_0.5.0.tgz |
Old sources: | imageseg archive |
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These binaries (installable software) and packages are in development.
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