Linking biodiversity patterns by autocorrelated random sampling

Linking objects in videos by importance sampling

Shape Matching by Random Sampling

In order to determine the similarity between two planar shapes, which is an important problem in computer vision and pattern recognition, it is necessary to first match the two shapes as good as possible. As sets of allowed transformation to match shapes we consider translations, rigid motions, and similarities. We present a generic probabilistic algorithm based on random sampling for matching ...

Monitoring autocorrelated processes using variable sampling schemes at fixed-times

Traditional control charts for process monitoring are based on taking samples of fixed size from the process using a fixed sampling interval. A recent development in control charts is the use of adaptive control charts, i.e. the variable sampling interval (VSI) and variable sampling size charts. This paper extends this idea to the autocorrelated process. We consider a time series model which is...

Tailored 3D Random Sampling Patterns for Nonlinear Parallel Imaging

measurements for subsampling with R=4, R=10 and R=18. Used sampling pattern (left), IRGN reconstruction (right). Fig. 2: Results of downsampling experiments of the brain for subsampling with R=10 and R=18. IRGN reconstructions of conventional regular Cartesian subsampling with autocalibration lines at the center of k-space (first column), radial subsampling (second column), variable density ran...

Generation and Analysis of Constrained Random Sampling Patterns

Random sampling is a technique for signal acquisition which is gaining popularity in practical signal processing systems. Nowadays, eventdriven analog-to-digital converters make random sampling feasible in practical applications. A process of random sampling is defined by a sampling pattern, which indicates signal sampling points in time. Practical random sampling patterns are constrained by AD...