Markov random field (MRF) models, including conditional random field models, are popular in computer vision. However, in order to be computationally tractable, they are limited to ...
We describe a unified framework for random interpretation that generalizes previous randomized intraprocedural analyses, and also extends naturally to efficient interprocedural an...
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...
Visual sensors provide exclusively uncertain and partial knowledge of a scene. In this article, we present a suitable scene knowledge representation that makes integration and fusi...
Energy-efficency is a key concern when designing protocols for wireless sensor networks (WSN). This is of particular importance in commercial applications where demonstrable retur...
Marco Zimmerling, Waltenegus Dargie, Johnathan M. ...