Traditional Markov network structure learning algorithms perform a search for globally useful features. However, these algorithms are often slow and prone to finding local optima d...
We present a novel and general optimisation framework for visual SLAM, which scales for both local, highly accurate reconstruction and large-scale motion with long loop closures. ...
Hauke Strasdat, Andrew J. Davison, J. M. M. Montie...
This paper considers additive factorial hidden Markov models, an extension to HMMs where the state factors into multiple independent chains, and the output is an additive function...
Monitoring is an issue of primary concern in current and next generation networked systems. For example, the objective of sensor networks is to monitor their surroundings for a va...
Ram Keralapura, Graham Cormode, Jeyashankher Ramam...
Linear scale-space theory provides a useful framework to quantify the differential and integral geometry of spatio-temporal input images. In this paper that geometry comes about by...
Alfons H. Salden, Bart M. ter Haar Romeny, Max A. ...