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Enabling automated herbarium sheet image post‐processing using neural network models for color reference chart detection
Applications in Plant Sciences ( IF 3.6 ) Pub Date : 2020-03-02 , DOI: 10.1002/aps3.11331
Dakila A Ledesma 1 , Caleb A Powell 2 , Joey Shaw 2 , Hong Qin 1
Affiliation  

Large‐scale efforts to digitize herbaria have resulted in more than 18 million publicly available Plantae images on sites such as iDigBio. The automation of image post‐processing will lead to time savings in the digitization of biological specimens, as well as improvements in data quality. Here, new and modified neural network methodologies were developed to automatically detect color reference charts (CRC), enabling the future automation of various post‐processing tasks.

中文翻译:

使用神经网络模型进行颜色参考图表检测的自动化植物标本室图像后处理

植物标本馆数字化的大规模努力已在 iDigBio 等网站上公开提供了超过 1800 万张植物图像。图像后处理的自动化将节省生物样本数字化的时间,并提高数据质量。这里开发了新的和改进的神经网络方法来自动检测颜色参考图表(CRC),从而实现未来各种后处理任务的自动化。
更新日期:2020-03-02
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