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Welcome to the Institut für Informations­verarbeitung!

We are living in the era of information: sharing and sending pictures, videos and multimedia data over the network has become part of our everyday lifes. This demands for information processing algorithms to encode, transmit, enhance and extract meaningful information from multimedia content. At our Institute we conduct cutting-edge research in the fields of audio and video signal processing, computer vision and machine learning. Broadly speaking, this involves designing intelligent coding algorithms to extract relevant information from the data.

Humans constantly extract meaningful information from visual data almost effortlessly. It turns out that simple visual tasks such as recognizing persons, detecting and tracking objects or understanding what is going on in the scene are extremely challenging problems for a computer. Training computers to process information as humans do has many potential applications in fields such as communication systems, medicine, artificial intelligence, robotics, surveillance, entertainment or sports science. It is therefore our ultimate goal to be able to emulate the human visual system with computational algorithms.

Our Institute was founded in 1973 by Prof. Dr.-Ing. Hans-Georg Musmann and is part of the Faculty of Electrical Engineering and Computer Science of the Gottfried Wilhelm Leibniz Universität Hannover. Today the group is headed by Prof. Dr.-Ing. Jörn Ostermann and Prof. Dr.-Ing. Bodo Rosenhahn and consists of about 30 researchers from more than seven different nationalities. Our technology is transferred in current and future telecommunication and digital systems, automation and interpretation tasks, remote sensing or medical image analysis. Approximately 70% of our research is funded by industry and national and international research grants.

Do you want to join us? We have open positions.

  • New Member at tnt: Let's welcome our new colleague Marius Lindauer. His research interest is Machine Learning, Reinforcement Learning and AutoML.

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