%0 Conference Proceedings
%9 ACTI : Communications avec actes dans un congrès international
%A Ariouat Sklab, Hanane
%A Sklab, Youcef
%A Pignal, M.
%A Vignes Lebbe, R.
%A Zucker, Jean-Daniel
%A Prifti, Edi
%A Chenin, Eric
%T Extracting masks from herbarium specimen images based on object detection and image segmentation techniques
%D 2023
%L fdi:010091410
%G ENG
%J Biodiversity Information Science and Standards
%@ 2535-0897
%P e112161 [5 ]
%R 10.3897/biss.7.112161
%U https://www.documentation.ird.fr/hor/fdi:010091410
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2024-08/010091410.pdf
%V 7
%W Horizon (IRD)
%X Herbarium specimen scans constitute a valuable source of raw data. Herbarium collections are gaining interest in the scientific community as their exploration can lead to understanding serious threats to biodiversity. Data derived from scanned specimen images can be analyzed to answer important questions such as how plants respond to climate change, how different species respond to biotic and abiotic influences, or what role a species plays within an ecosystem. However, exploiting such large collections is challenging and requires automatic processing. A promising solution lies in the use of computer-based processing techniques, such as Deep Learning (DL). But herbarium specimens can be difficult to process and analyze as they contain several kinds of visual noise, including information labels, scale bars, color palettes, envelopes containing seeds or other organs, collection-specific barcodes, stamps, and other notes that are placed on the mounting sheet. Moreover, the paper on which the specimens are mounted can degrade over time for multiple reasons, and often the paper's color darkens and, in some cases, approaches the color of the plants.
%$ 122 ; 082