We develop a Bayesian model of digitized archival films and use this for denoising, or more specifically de-graining, individual frames. In contrast to previous approaches our mod...
Teodor Mihai Moldovan, Stefan Roth, Michael J. Bla...
In this work, we propose an unsupervised Bayesian model for the detection of moving objects from dynamic scenes. This unsupervised solution is a three-step approach that uses a st...
We propose a new methodology for fusing temporally changing multisensor raster and vector data by developing a spatially and temporally varying uncertainty model of acquired and t...
The output of boolean association rule mining algorithms is often too large for manual examination. For dense datasets, it is often impractical to even generate all frequent items...
Computational simulation of time-varying physical processes is of fundamental importance for many scientific and engineering applications. Most frequently, time-varying simulation...