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The amount of digital images in our daily live has grown exponentially during the last years, cameras are low-cost sensors which are present everywhere, and billions of images are daily shared on social media. Also the industrial interest in methods for digital image and video processing is increasing strongly. As a consequence, the need for algorithms that automatically improve, analyze, and interpret images also rises more and more. Fortunately, the research field of computer vision has also advanced strongly during the last decade and many things which were not feasible a few years ago are suddenly achievable. However, when it comes to seemingly simple daily live questions such as "how many objects are on this table?", current systems reach their limits and it shows that the human visual system still outperforms machines clearly. - In my research group, we focus on biologically inspired methods for computer vision. That means, we develop algorithms that follow mechanisms of human vision, outgoing from psychophysical and neurobiological findings. Topics of our research include the detection of saliency in images and the discovery of objects. We focus on methods for mobile systems, such as wearable cameras or autonomous service robots.
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