In this paper, we describe a dynamic Bayesian network or DBN based approach to both two-hand gestures and onehand gestures. Unlike wired glove-based approaches, the success of cam...
This paper presents a technique for dynamically reconfiguring search spaces in order to enable Bayesian autonomous search and tracking missions with moving targets. In particular, ...
Benjamin Lavis, Tomonari Furukawa, Hugh F. Durrant...
Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with heterogeneity and non-stationarity in temporal processes. Various ap...
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
In many domains, a Bayesian network's topological structure is not known a priori and must be inferred from data. This requires a scoring function to measure how well a propo...