Stanley J. Osher is a Professor of Mathematics and Director of Applied Mathematics at the University of California, Los Angeles. He is the co-inventor and a principle developer of a range of methods that have a huge impact on the field of imaging and image processing. His papers have been cited more than 70.000 times. To name just a few of his contributions, he developed (1) level set methods, (2) total variation based image processing techniques, and (3) fast algorithms for L1 optimization. The latter technique has lead to a recent revolution in tomographic image reconstruction. It will be the main topic of his talk in the BioImage Geek Seminar Series.
The campus-wide BioImage Geek Seminar Series hosts distinguished experts speaking about exciting methodological advances in the fields of microscopic imaging, image reconstruction, image analysis, computer vision and machine learning in the context of cell biology.
Our next speaker will be Björn Andres from the Computer Vision and Multimodal Computing department at the Max Planck Institute for Informatics, Saarbrücken:
Host: Carsten Rother, Computer Vision Lab Dresden.
Everybody is welcome.
Phenomenal progress in microscopy has greatly improved biologists' ability to image the structures and processes of life. Today, much biological insight lies hidden in big image data, too large to be processed entirely by hand. Mathematical models for computational and computer-assisted biological image analysis are the subject of this talk. Specifically, it is demonstrated by applications in segmentation, tracking and reconstruction how combinatorial optimization techniques facilitate the automated and assisted analysis of biological images by allowing biologists to incorporate prior knowledge and hard constraints. It is also discussed how knowledge obtained from a small amount of hand-processed data can be transferred to a large amount of unlabeled data by machine learning techniques.
We are happy to announce our CVPR workshop. Paper submission is open... we are looking forward receiving your submissions!
Matthias W. Seeger recently joined the Machine Learning Group at Amazon Berlin. Previously, he was an assistant professor at the School of Computer and Communication Sciences at the Ecole Polytechnique Federale de Lausanne. Dr. Seeger has made seminal contributions to theory and practice of Gaussian process models, PAC-Bayesian learning theory, and variational approximations of Bayesian inference for sparse generalized linear models. His work applies to problems in computer vision and imaging, compressive sensing, and bioinformatics. E.g., he has shown that magnetic resonance imaging sequences can be optimized by means of Bayesian experimental design. His talk in the BioImage Geek Seminar Series will be a tutorial on variational Bayesian inference and experimental design.
In our new campus-wide "BioImage Geek" Seminar Series, we host distinguished experts speaking about exciting methodological advances in the fields of microscopic imaging, image reconstruction, image analysis, computer vision and machine learning in the context of cell biology. We aim at hosting six speakers per year. Invitees are selected by the Seminar Committee formed by the Myers lab and the Sbalzarini lab at MPI-CBG, and Carsten Rother's Computer Vision Lab at TUD.
Everybody is welcome!
Curtis Rueden, Lee Kamentsky, Kevin Mader, Christian Dietz, Johannes Schindelin, Tobias Pietzsch, Florian Jug and many others will get together in Konstanz from the 21st until the 27th of January to make yet another step towards providing powerful image and data analysis software tailored for the natural sciences. #UnderappreciatedSoftwareHeros
There will also be a Mini-Symposium in order to spread the word... 😉
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On August 28th 2014 the construction work for our new CSBD building where officially launched.
See below what MDR aired on the evening of this historical day...
...was a big success! People likes his style, mainly the ones who did not know him well before.