We investigate theoretically some properties of variational Bayes approximations based on estimating the mixing coefficients of known densities. We show that, with probability 1 a...
— In this paper we present an approach for localizing a sensor network augmented with a mobile robot which is capable of providing inter-sensor pose estimates through its odometr...
David Meger, Dimitri Marinakis, Ioannis M. Rekleit...
In this paper, we formulate the shape localization problem in the Bayesian framework. In the learning stage, we propose the Constrained RankBoost approach to model the likelihood ...
In this paper, we investigate to which extent the "raw" mapping of Taylor series coefficients into jet-space can be used as a "language" for describing local i...
We propose a new method for rapid 3D object indexing that combines feature-based methods with coarse alignment-based matching techniques. Our approach achieves a sublinear complexi...
Bogdan Matei, Ying Shan, Harpreet S. Sawhney, Yi T...