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Semantic Multimedia Analysis
In our core resaerch area Semantic Multimedia Analysis, we address the extraction of semantic information in video data. This is known as one of the most difficult computational problems, because automatically computable features (e.g., colour, edge, motion, audio etc.) do not have a direct relation to semantic information perceived by human (e.g., object, action, scene, event etc.).g

Multimodal Scene Analysis
Recognizing objects in scenes is one of the most fundamental and challenging problems in computer vision. The main objective of our core research area Multimodal Scene Analysis is the detection, recognition and analysis of 2D and 3D objects in static and natural scenes.

On the one hand, the demographic trends and the shortage of medical staff (especially in rural areas) critically challenge healthcare systems in industrialised countries. On the other hand, the digitalisation of our society progresses with a tremendous speed, so that more and more health-related data are available in a digital form. For instance, people wear intelligent glasses or/and smart watches, provide digital data with standardised medical devices (e.g., blood pressure and blood sugar meters following the standard ISO/IEEE 11073) or/and deliver personal behavioural data by their smartphones. Pattern recognition algorithms that automatically analyse and interpret that huge amount of heterogeneous data towards prevention (early risk detection), diagnosis, assistance in therapy/aftercare/rehabilitation as well as nursing will experience an extremely high scientific, societal and economic priority in the near future.