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| Marchant, Ross; Tetard, Martin; Pratiwi, Adnya; Adebayo, Michael; De Garidel-thoron, Thibault. |
| Manual identification of foraminiferal morphospecies or morphotypes under stereo microscopes is time consuming for micropalaeontologists and not possible for nonspecialists. Therefore, a long-term goal has been to automate this process to improve its efficiency and repeatability. Recent advances in computation hardware have seen deep convolutional neural networks emerge as the state-of-the-art technique for image-based automated classification. Here, we describe a method for classifying large foraminifera image sets using convolutional neural networks. Construction of the classifier is demonstrated on the publicly available Endless Forams image set with a best accuracy of approximately 90 %. A complete automatic analysis is performed for benthic species... |
| Tipo: Text |
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| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00655/76687/77826.pdf |
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