Researcher, Engineer, Tinkerer, Scholar, and Philosopher.
PhD in Applied Mathematics, 2015
Saarland University
MSc in Applied Mathematics, 2010
Saarland University
BSc in Pure Mathematics, 2008
University of Luxembourg
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A weather station with a web frontend
A collection of notes and investigations on numerical linear algebra and optimization related topics.
Recently I acquired a Raspberry Pi 4 and decided to build a tiny computer cluster out of it. The goal is to do play around a bit with parallel computing technology.
Besides my research in computer vision related tasks such as optical flow, photometric stereo, and shape matching and my focus on PDE-based compression, I have also ventured in other image processing tasks.
I have used machine learning techniques in various projects. Our most successful applications were in the context of quantizing colour values for optimized inpainting and in accoustic source characterizations. Source Code The source code for clustering methods used for quantizing optimal masks can be found here.
Let us consider a microphone array comprising $n$ microphones at known locations (see figure above). These microphones register the sound that is emitted by a number of sources with unknown locations.
Floating point computations on computers may behave differently than one might expect. Every software developer should be aware of these since computed results may be off by orders of magnitude in the worst case.
I develop a small toy setup with a pair of Arduinos and some ultrasound sensors to do object localization. The goal is to have at least 4 echo distances in each measurement to assert a unique solution of the localization equations.
We have investigated high performing optimization algorithms and matrix differential calculus technique in the context of Photometric Stereo and presented the results at the BMVC 2016 Source Code A github repository with the code is maintained by Yvain Quéau.
I have been using emacs since about 2008. My current configuration is stored in an org file. The git repository is available here. A webpage containing all settings is accessible here.
I’ve developed optimization algorithms for variational optical flow models based on the split Bregman algorithm in my Master thesis. A follow-up investigation on the necessity of certain intermediate filtering steps was published at the EMMCVPR 2011.
The main task in three-dimensional non-rigid shape correspondence is to retrieve similarities between two or more similar three-dimensional objects. We analysed how well partial differential equations may be used to solve this problem.
This is a complete theme for the beamer package for LaTeX. I have used in on for regularly for several years now. The theme includes an outer, inner, font and colour theme.
It is possible to compress/inpaint images from very little data. In order to obtain reconstructions that are comparable to the original image it is necessary to optimize the underlying interpolation data.
Estimating the shape and appearance of a three dimensional object from flat images is a challenging research topic that is still actively pursued. Among the various techniques available, Photometric Stereo is known to provide very accurate local shape recovery, in terms of surface normals. In this work, we propose to minimise non-convex variational models for Photometric Stereo that recover the depth information directly. We suggest an approach based on a novel optimisation scheme for non-convex cost functions. Experiments show that our strategy achieves more accurate results than competing approaches.
This work analyses several approaches for determining optimal sparse data sets for image reconstructions by means of linear homogeneous diffusion. Two optimisation strategies for finding optimal data locations are presented. The first one impresses through its simplicity and is based on results from spline interpolation theory. However, this approach can only be applied to one dimensional strictly convex and differentiable functions. Due to these restrictions we derive an alternative approach which uses findings from optimal control theory. This new algorithm can be applied on arbitrary signals. Both approaches are analysed for their convergence behaviour. Further, we discuss the problem of selecting good data values for fixed data positions. This problem can be analysed as a least squares problem. An important relationship between the optimal data locations and the data values is derived and we present efficient numerical schemes to obtain these values. Finally, we present a image compression approach based on the findings from this work. Experiments show that is possible to outperform popular compression algorithms.
If you want to tell me something, write me an e-mail. My public PGP Key.
Full contact details are also available in the Terms section.