Go to content
UR Home

GinJinn: An object‐detection pipeline for automated feature extraction from herbarium specimens

URN to cite this document:
urn:nbn:de:bvb:355-epub-440677
DOI to cite this document:
10.5283/epub.44067
Ott, Tankred ; Palm, Christoph ; Vogt, Robert ; Oberprieler, Christoph
[img]
Preview
License: Creative Commons Attribution 4.0
PDF - Published Version
(665kB)
Date of publication of this fulltext: 12 Jan 2021 14:54



Abstract

Premise The generation of morphological data in evolutionary, taxonomic, and ecological studies of plants using herbarium material has traditionally been a labor‐intensive task. Recent progress in machine learning using deep artificial neural networks (deep learning) for image classification and object detection has facilitated the establishment of a pipeline for the automatic recognition and ...

plus


Owner only: item control page
  1. Homepage UR

University Library

Publication Server

Contact:

Publishing: oa@ur.de
0941 943 -4239 or -69394

Dissertations: dissertationen@ur.de
0941 943 -3904

Research data: datahub@ur.de
0941 943 -5707

Contact persons