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Title: RaDSS V02: A Radiolarian Classifier Using Convolutional Neural Network
Authors: Quisote, Micah
Keywords: Radiolarian
Deep Learning
Convolutional Neural Networks
Image Recognition
Image processing
Issue Date: May-2018
Abstract: Radiolarian assemblages have played a signi ficant role as a biostratigraphic and paleoenvironmental tool used in age-dating, correlation, and studying deep-sea sedimentary rocks that lacks calcareous fossils. The species rapid classi fication would allow micropaleontologists to proceed further into studying the structure and way of living of these Radiolarians. RaDSS V02 is a deep learning based system that could help researchers in classifying Radiolarian species' microfossil images through image processing and convolutional neural network.
Appears in Collections:Computer Science SP

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