In the European Digital Innovation Hub "AI5production", 17 renowned scientific institutions have joined forces to support manufacturing companies on their digitalization journey with the help of AI technologies.
Within the framework of “AI for Green”-funded research project develops AI-based solutions aimed at optimizing free satellite data for monitoring agricultural areas of all sizes.
The project REINFORCE researches how reinforcement learning and human-centered visualization methods can be used to solve complex control problems in an efficient, fast, and flexible way.
The research project CognitiveXR focuses on developing a platform that enables cognitive augmentation in the smart city domain by seamlessly integrating augmented reality, edge computing, and artificial intelligence.
The aim of the application project IVC Multi is to research novel intelligent visual computing methods supporting decision-making in automotive industry, medicine, and life sciences based on ensembles of heterogeneous, multi-scale and/or multi-temporal data.
The aim of the project IVC Stream is to research novel visual computing solutions for simulation and measurement data.
This project aims at accelerating and automating image-based decision making with an application focus on medicine, recycling and quality assurance processes in manufacturing.
The strategic project of the area is the organizational and scientific hub for the realization of the area-wide intelligent visual computing approach for analytics and modelling based on ensembles of dense grid-based data, derived data, and digital embedding.
The "Mars-DL" project is investigating how a deep learning system can support the research work of planetary scientists through object and pattern recognition. For this project VRVis has extended the functionality of PRo3D to automatically render shatter cone training images.
VRVis a founding member of the Austrian BioImaging/CMI, which is a professional consortium of multiple Austrian science institutions and the official Austrian Euro-BioImaging initiative.
Visual computing for medicine: image processing solutions for new applications in radiology.
In this project we develop new methods from the field of visual analysis and machine learning to automate the quality control and quality assurance of glass articles.
The long-term vision of this applied research project is to use available data resources to improve image-based diagnostics based on complex data in daily clinical routine.
Visual computing techniques for the automated detection of osteoporosis and osteoarthritis.