Abstract
Biodiversity measuring calls for massive capture of in situ fauna observations over a long period. The present research aims at reducing the cost and the bias of the measures by limiting human intervention for producing those observations. It is based on a integrated infrastructure made of an automated underwater camera trap and a collaborative visual interface to analyze, process and manage the observations. In a latter stage, the infrastructure will be complemented with full image processing and machine learning capabilities to automate as much as possible the handling and exploitation of the observations.
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Didry, Y., Mestdagh, X., Tamisier, T. (2019). Newtrap: Improving Biodiversity Surveys by Enhanced Handling of Visual Observations. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2019. Lecture Notes in Computer Science(), vol 11792. Springer, Cham. https://doi.org/10.1007/978-3-030-30949-7_32
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DOI: https://doi.org/10.1007/978-3-030-30949-7_32
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