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Visual Scene Analysis


Workshop on Imaging and Vision in Siegen!

From March 14th to March 16th, 2018, I am organizing a workshop on Imaging and Vision from Theory to Applications in Siegen. Its goal is to foster the exchange and collaboration between researchers from applied math, imaging, vision and graphics. Please visit the workshop's website to find out more!


Open PhD/PostDoc position

We are always looking for highly motivated PhD candidates with an interest in numerical methods for Visual Computing and outstanding mathematical skills. If you are interested, please send an email to michael.moeller@uni-siegen.de, and include your CV as well as a letter describing your interest in the field. 


Research

The visual scene analysis group conducts research in the field of mathematical image processing, computer vision, and machine learning with a special focus on the development of energy minimization methods for improving and analysing digital images. In this context we are interested in convex relaxations, functional lifting and multiscale methods as well as the development of efficient convex and nonconvex optimization algorithm for the solution of the corresponding energy minimization problems. Please visit our publications page to find out more about our recent research. 


Teaching

In the summer semester 18 we will offer a masters course on convex optimization, which is fundamental to the understanding of optimization methods in general and can serve as the basis for many exciting research questions in computer vision and machine learning!


Bachelor or Master Thesis

Feel free to contact me at michael.moeller@uni-siegen.de if you are interested in writing your Bachelor or Master thesis in the field of image processing or computer vision! We have several exciting topics! Please find a list of examples below. I am happy to explain these (sometimes technically sounding) topics in more detail. 

For instance

  • In deep learning, e.g. familiarize yourself with a deep learning framework such as https://www.tensorflow.org, or http://pytorch.org/, and improve certain image reconstruction tasks, e.g. working on decompressing images with an architecture motivated by variational decompression methods. For the latter a particularly interesting interdisciplinary project could be a collaboration with the group of Prof. Weinberg (Festkörpermechanik) on the automatic analysis of open-pore polyurethane foam. A German description can be found here
  • For an applied project, consider the tracking of the common swift in videos taken from a bridge near Biggesee. In particular, tracking the swifts will allow biologists to analyze the forage of the swifts in a level of detail that is currently impossible for such small birds. This project would be carried out in close collaboration with Prof. Klaudia Witte from the in Institute for Biology. 
  • In 3D reconstruction, e.g. familiarize yourself with the relation of images and the camera pose to real world coordinates and try to reimplement an energy minimization approach to surface reconstruction similar to https://www.youtube.com/watch?v=TGg-ujjSsOM (but not in real-time). Extensions of such techniques are able to map large scale geometric features, see e.g. https://www.youtube.com/watch?v=GnuQzP3gty4
  • In optimization, e.g.
    • on joint optimization methods for biconvex or nonconvex problems such as blind deblurring or super resolution. Some suprising results show that there is a gap between good results and faithful optimization (http://www.cvg.unibe.ch/dperrone/tvdb/), which could be investigated in more detail!
    • on optimizing deeply nested functions (as frequently arising in deep learning applications) with proximal splitting methods.
    • on efficient ways to optimize a certain class of constrained convex functions whose proximal operator does not have a closed form.
    • on optimizing nonconvex functions by representing the underlying problem in a higher dimensional space, also known as lifting. See https://arxiv.org/pdf/1512.01383.pdf for an example of a recent work on this topic. I can give clear instructions of how a project on a related topic can look like. 
  • In combining optimization and deep learning methods, e.g. by studying and extending our recent work on using denoising networks as proximal operators, see https://arxiv.org/abs/1704.03488.
  • On multiscale methods for inverse problems, extending the current theory on nonlinear spectral decompositions (see, e.g. https://arxiv.org/pdf/1510.01077.pdf). Such a project could be application driven (https://arxiv.org/pdf/1703.08001.pdf), or based on some mathematical analysis of generalized eigenfunctions, see https://arxiv.org/pdf/1601.02912v1.pdf

If you have ideas for your own research project, let me know!