We propose a new method for recovering a 3-D object shape from an image sequence. In order to recover high-resolution relative depth without using the complex Markov random field...
— Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a...
Andrea Censi, Daniele Calisi, Alessandro De Luca, ...
The practical success of broadcast encryption hinges on the ability to (1) revoke the access of compromised keys and (2) determine which keys have been compromised. In this work w...
This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that i...
The paper presents an efficient goal oriented algorithm for symbolic propagation in Bayesian networks. The proposed algorithm performs symbolic propagation using numerical methods...