The growing reliance of networked applications on timely and reliable data transfer requires the underlying networking infrastructure to provide adequate services even in the pres...
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
We present a near linear time algorithm for constructing hierarchical nets in finite metric spaces with constant doubling dimension. This data-structure is then applied to obtain...
Abstract—This work proposes a novel connectivity-based localization algorithm, well suitable for large-scale sensor networks with complex shapes and non-uniform nodal distributio...
In this paper we propose and evaluate an algorithm that learns a similarity measure for comparing never seen objects. The measure is learned from pairs of training images labeled ...