Multidimensional Image Processing
Learning-based Image Analysis for Industry and the Life Sciences
Vast numbers of images are now routinely acquired to explore complex spatially organized processes in areas as diverse as biology and industrial quality control. We develop algorithms to solve real-world image analysis problems from these domains. Beyond conventional images, we often deal with data where a multitude of observations are available for each element, such as multidimensional or spectral images.
Whenever reasonable, we use techniques from statistical or machine learning to build such automated diagnostic systems. Our ambition is to make the machine learning process as robust and user-friendly as possible: we hence develop algorithms for active and semi-supervised learning that can benefit even from small or partially annotated training sets while exploiting spatial context. We seek to make state-of-the-art algorithms available to practitioners in the guise of ilastik.
We enjoy and cultivate very close collaboration with our experimental partners from biology and engineering and are grateful for the constant flow of challenging problems and constructive criticism they provide. A long-standing partner and sponsor of this group is the Robert Bosch GmbH, for whom we have developed and implemented a number of systems that successfully run in a production environment, 24/7 and all year round.
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Last update: 10.09.2013, 15:04