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
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:

Reference manual:	imageseg.pdf
Vignettes:	A sample session in imageseg

Downloads:

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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