Speaker: Daniel Cornel
In this thesis, an importance sampling algorithm based on a threshold matrix is discussed. The matrix originally designed for ordered dithering is created off-line by a repulsive force field, after which sample points can be obtained by simple thresholding. By the design of the matrix, these samples are distributed irregularly and roughly equidistantly, which is desirable for stochastic sampling. Non-uniform distributions can be achieved by thresholding the matrix with an importance function rather than a constant value. This allows the generation of multidimensional sampling patterns at very low on-line computation cost as compared to importance sampling strategies based on transforming initial (quasi-)random sample sets. The quality of the resulting sample point sets is quantified with the help of established metrics in order to compare the different approaches.