Imports: Rcpp (>= 0.12.14), magrittr, fftwtools, tesseract

person("Image Processing On Line", role = "cph"),
person("Pascal", "Getreuer", email = "getreuer@gmail.com", role = c("ctb", "cph")))
Description: Providing advanced functions for image processing based on the package 'imager'.
Imports: fftwtools, magrittr, Rcpp (>= 0.12.14)
Suggests: testthat (>= 2.0.0), knitr, rmarkdown, tesseract

Description: Provides advanced functions for image processing based on the package 'imager'.

Title: Deep Learning Models for Image Segmentation
person("Juergen", "Niedballa", email = "niedballa@izw-berlin.de", role = c("aut", "cre"),
person("Jan", "Axtner", email = "axtner@izw-berlin.de", role = c("aut"),
person("Leibniz Institute for Zoo and Wildlife Research", role = "cph")
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>. 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.
Imports: grDevices, keras, magick, magrittr, methods, purrr, stats,
tibble, foreach, parallel, doParallel, dplyr
Leibniz Institute for Zoo and Wildlife Research [cph]

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.