Diploma and PhD Theses

Fraunhofer ITWM

Graduation work topics

We are looking for Research Assistants

Photon-tomography of a lichen

Geometrical analysis of volume images

The group "Analysis and modelling of microstructures" offers continuously interesting topics in 3D image analysis and microstructure modelling.The microstructure of modern materials is highly complex and significantly determines macroscopic materials properties like mechanical strength or thermal conductivity. Imaging methods like micro-computed tomography yield three dimensional images of the microstructure. Stochastic geometric models are used to study the influence of the microstructure on those macroscopic properties. Tasks are open in both determining useful geometric characteristics from image data as well as choosing, simulating, and fitting of these models to real microstructures.A stimulating interdisciplinary work environment is waiting for you. You have the chance to work at the interface between academic and industrial research.

Further Topics Tomographic image of the pore system

Analysis of the microstructure of polar ice

How differs the microstructure of polar ice in different depths?
Does ice from the Antarctic differ from ice from Greenland?
Which effect has the climate (temperature, precipitation,...) on the ice structure?

Concrete with inclusions

Analysis of porous materials using granulometry

How can the pore space of porous materials be characterized?
Which relations exists between the microstructure and the material properties, e.g. permeability?

Ultrasonic Imaging

Modeling the scattering at rough cracks

The roughness of cracks in metals leads to an increase of the diffuse-scattered ultrasonic wave fields and problems in view of the localisation of cracks, their imaging and their sizing. How is it possible to optimize ultrasonic imaging techniques for these applications by using simulations?

Tropfenverteilung in chemischer Produktionsanlage

Industrial image processing

Efficient finding of objects

How can rotated and translated copies of objects be found efficiently in digital images? How can size distributions of frequently appearing simple objets (like circles, for example) in image be estimated?

Further Topics

Coordinate systems for color spaces

During the analysis of lumber, textiles and other colored objects it is necessary do differ between least shades. The standard RGB coordinates of the color space are not best suitable for this task.
This leads to the question for a new coordinate system in a three dimensional RGB space, which simplifies and upgrades the separation of relevant colors. The difficulty is not just to find an adequate transformation, but also in providing a compact and plain description.

Information preserving Downsampling

While processing big data sets the data volume has to be reduced fastly, e.g. for holding the time limits. An easy method for this is the so called downsampling. A great disadvantage of this method is that structures are destroyed that are finer than resolution limit.
How do optimal (non linear) projections that reduce the number of pixels but achieve as much as possible information of the picture look like?

Highdimensional search

In general image processing routines depends on some parameters. The common case is e.g. the threshold that binarizes the image. It is desired to find the optimal set of parameters for a given algorithm with a quantity of parameters and a set target. Therefore, suitable retrieval strategies have to be found, in order that the retrieval in usually high dimensional parameter space is be possible. Modern heuristic methods like evolutionary algorithms, particle swarm optimization and generative neural networks are appropiate.

X-ray image of frozen food with contaminants

Simulation of contaminants in X-ray images

In the production line, an error appears only in 0.1% of all cases, and it is not possible to check every product. How is it possible to obtain enough example images to develop image processing algorithms to robustly find these error? How can realistic deteriorations of X-ray images be simulated with the computer?

Locally adaptive combination of filters for denoising

What is the best way to remove noise from data? How can the quality of image denoising be measured? How can we simplify images and keep important information in the image during this process?

Artefacts from denoising