Sort by
Refine Your Search
-
Employer
-
Field
-
designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
-
. Analysis of images will investigate the efficacy of manual digital approaches (e.g., Dot Dot Goose) and the development of a marine litter characterisation and quantification algorithm for automated analysis
-
Assessment Systems: Toward Trustworthy AI for Complex Educational Evaluation Image and Video Analysis Using Machine Learning Algorithms Mathematical and Computational Neuroscience, from neural data and network
-
for Pollinator Monitoring: Train and optimise deep learning models for pollinator detection and classification using annotated image datasets. Post-processing object tracking algorithms will be incorporated