Automatically identifying various organisms in underwater images is challenging due to the inherent complexity of benthic images, often containing a large amount of visual elements.
ANERIS aims to create AIES-MAC - a technology to detect and identify macro-organisms in single images obtained from citizen smartphones or EMUAS, after processing by ATIRES, to support the AMOVALIH classification service by uploading observations for validation. (1) Within EMUAS, embedded detection (likely image differencing-based) identifies events such as species presence or macro-organisms entering the field of view, triggering video recording. (2) Recognition of Regions of Interest (ROIs) containing one macro-organism will involve segmenting the organisms and extracting size, shape, texture, or other parameters for characterization and taxonomic classification.
Currently, AIES-MAC is testing preliminary algorithms using a test database of offline images & videos, with and without image restoration provided by ATIRES.
Governmental Authorities: Ocean Best practices from UNESCO
Academia: All academic institutions for which such non invasive observations would be of interest
Industries: Citizens, NGOs, associations, startups