Research of the MIA Group

We perform research on mathematically well-founded methods in image processing and computer vision. The main focus is on techniques using partial differential equations and variational methods. We are interested in all aspects of these techniques, including mathematical modeling, well-posedness analysis, efficient algorithms for sequential and parallel computer architectures, and medical applications.

Image Processing of Tensor Fields

Matrix-valued data sets (so-called tensor fields) are gaining increasing importance in digital imaging. In our group we have developed a number of novel image processing methods that work directly on tensor fields ...

Mathematical Morphology

Mathematical morphology originated from the study of porous media in the mid-sixties. Since then it has undergone a tremendous development resulting in efficient tools for modern image processing and analysis ...

Relations between Discontinuity Preserving Methods

We consider a classical task of signal denoising: create an estimate u of an original signal z from its noisy measurement f, where f = z + n and n denotes an additive noise function ...

Image Enhancement

Image enhancement is the improvement of digital image quality (wanted e.g. for visual inspection or for machine analysis), without knowledge about the source of degradation. If the source of degradation is known, one calls the process image restoration...

Optic Flow

Correspondence problems are one of the key problems in computer vision. They appear for example in motion analysis, image matching, or stereo vision. The goal is to assign structures in one image to structures in a second image, in a way that corresponding structures are as similar as possible ...

Image Segmentation

The partitioning of an image into meaningful regions is one of the principal computer vision tasks. On the way from the raw pixel data up to a meaning of the image, it is necessary to reduce the amount of information and to represent it in a compact manner...

Stereo Reconstruction

Stereo reconstruction is based on the same principle as the human visual system uses for depth recovery. Two cameras viewing the same scene under different perspective transformations are used for being able to reconstruct 3D objects...

Scientific Computing

For many tasks in the area of image processing and computer vision a fast and accurate computation of the results is required. One may claim that the rapid progress in the sector of computer engineering will solve this problem by allowing algorithms to become faster year by year...

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