Automatic plankton quantification using deep features
Author:
Affiliation:
1. ARTIFICIAL INTELLIGENCE CENTER, UNIVERSITY OF OVIEDO, CAMPUS DE VIESQUES, GIJóN, ASTURIAS, SPAIN
2. BIOLOGY DEPARTMENT, WOODS HOLE OCEANOGRAPHIC INSTITUTION, WOODS HOLE, MA, USA
Abstract
Funder
National Science Foundation
National Oceanic and Atmospheric Administration
Simons Foundation
Publisher
Oxford University Press (OUP)
Subject
Ecology,Aquatic Science,Ecology, Evolution, Behavior and Systematics
Link
http://academic.oup.com/plankt/article-pdf/41/4/449/30279440/fbz023.pdf
Reference52 articles.
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3. Quantification in-the-wild: Data-sets and baselines;Beijbom,2015
4. Quantification via Probability Estimators
5. RAPID: research on automated plankton identification;Benfield;Oceanography,2007
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