DESCRIPTION

Package: imageseg
Type: Package
Title: Deep Learning Models for Image Segmentation
Version: 0.5.1
Authors@R: c(
            person("Juergen", "Niedballa", email = "niedballa@izw-berlin.de", role = c("aut", "cre"),
                   comment = c(ORCID = "0000-0002-9187-2116")),
            person("Jan", "Axtner", email = "axtner@izw-berlin.de", role = c("aut"),
                   comment = c(ORCID = "0000-0003-1269-5586")),
            person("Leibniz Institute for Zoo and Wildlife Research", role = "cph")
            )
Maintainer: Juergen Niedballa <niedballa@izw-berlin.de>
Description: A general-purpose workflow for image segmentation using TensorFlow models based on the U-Net architecture by Ronneberger et al. (2015) <arXiv:1505.04597> and the U-Net++ architecture by Zhou et al. (2018) <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.
License: MIT + file LICENSE
BugReports: https://github.com/EcoDynIZW/imageseg/issues
Encoding: UTF-8
Imports: grDevices, keras, magick, magrittr, methods, purrr, stats, tibble, foreach, parallel, doParallel, dplyr
Suggests: R.rsp, testthat
VignetteBuilder: R.rsp
RoxygenNote: 7.2.1