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GinJinn: An object‐detection pipeline for automated feature extraction from herbarium specimens
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 ob...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
John Wiley and Sons Inc.
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328649/ https://www.ncbi.nlm.nih.gov/pubmed/32626606 http://dx.doi.org/10.1002/aps3.11351 |