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Project

Machine learning for automatic semantic segmentation

AI systems often have to cope with an overwhelming amount of input data, while only a fraction of it is relevant for further processing. Therefore automated data decimation processes are required, which discard irrelevant samples (data points), remove redundancy by fusing multiple input sources/features, and perform a segmentation of time-series into higher-level objects. In this project, we will automate these segmentation processes as much as possible, with a focus on automatically splitting (multi-modal) time series or images into segments of variable sizes, within which the statistics are homogeneous across each segment.
Date:21 Feb 2020 →  18 May 2020
Keywords:Artificial intelligence, Segmentation
Disciplines:Biomedical signal processing, Pattern recognition and neural networks
Project type:PhD project