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.">

imageseg: Deep Learning Models for Image Segmentation (original) (raw)

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 ORCID iD [aut, cre], Jan Axtner ORCID iD [aut], Leibniz Institute for Zoo and Wildlife Research [cph]
Maintainer: Juergen Niedballa
BugReports: https://github.com/EcoDynIZW/imageseg/issues
License: MIT + file
NeedsCompilation: no
Materials: README NEWS
CRAN checks: imageseg results

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