We propose an approach to identify and segment objects from scenes that a person (or robot) encounters in Activities of Daily Living (ADL). Images collected in those cluttered sce...
Abstract. We present two data-driven importance distributions for particle filterbased articulated tracking; one based on background subtraction, another on depth information. In ...
The supervised learning paradigm assumes in general that both training and test data are sampled from the same distribution. When this assumption is violated, we are in the setting...
Abstract: We propose a new system that is able to handle the entire Personal Dataspace of a user. A Personal Dataspace includes all data pertaining to a user on all his disks and o...
Jens-Peter Dittrich, Lukas Blunschi, Markus Fä...
We propose an efficient multiscale image disparity estimation algorithm that estimates the local translations needed to align different regions in two images. The algorithm is bas...