Computer Graphics
GPGPU Geometric Refinement
GPUs process data of the same resolution very quickly with massive data parallel execution. But even the massive parallelism cannot compete with adaptive methods when the data size grows cubically under uniform refinement. This project develops parallel refinement strategies with grids and particles that allow to introduce higher resolution in only parts of the computational domain.
Visualization
The choice of visualization methods and parameters is already a part of the interpretation process of the data, as it emphasizes certain structures and subdues others. This can lead to positive effects uncovering otherwise unconceivable relations in the data, but may also produce false evidence. Combinations of multiple methods, and data based parameter controls try to limit this danger.
Reconfigurable Computing
This projects investigates how the enormous parallelism of reconfigurable hardware can be harnessed to accelerate PDE solvers. Both fine- and coarse-grained architectures are examined. The performance is very convincing but for complex problems higher level programming languages for these devices are required.
GPGPU Image Processing
★Pioneering work on PDE solvers with GPUs★ The data parallelism in typical image processing algorithms is very well suited for data-stream-based architectures. PDE based methods for image denoising, segmentation and registration have been thus accelerated on graphics cards.
GPGPU Computer Vision
Although graphics processor units (GPUs) are still very restricted in data handling some strategies allow the focusing of processing on data-dependent regions of interest. Thus computer vision algorithms which require computations on changing regions of interest can already benefit from the high GPU performance. Current implementations comprise the Generalized Hough Transform, skeleton computation and motion estimation.