This paper describes techniques for fusing the output of multiple cues to robustly and accurately segment foreground objects from the background in image sequences. Two different m...
We study complexity and approximation of queries in an expressive query language for probabilistic databases. The language studied supports the compositional use of confidence com...
In this paper we tackle the problem of scheduling a periodic real-time system on identical multiprocessor platforms, moreover the tasks considered may fail with a given probabilit...
Treating visual object tracking as foreground and background classification problem has attracted much attention in the past decade. Most methods adopt mean shift or brute force s...
A general and expressive model of sequential decision making under uncertainty is provided by the Markov decision processes (MDPs) framework. Complex applications with very large ...