This paper explores the possibility of a linear model as a solution to the problem of appearance-based pose estimation. The parametric eigenspace method (or its extensions that are based on correlation between images) has been widely used and yields successful results for the pose estimation problem. On the other hand, the method has some problems. One is that large computational cost and stora...

The recovery of geometric structure from noisy data poses difficult non-linear statistical estimation problems. This paper describes a novel, robust, low computational cost approach for finding the geometric structure of an axially symmetric pot from a small fragment of it (an unorganized set of 3D points). This problem is of great archaeological importance to the study of the hundreds and thou...

The main problem faced by naval radars is the elimination of the clutter input which is a distortion signal appearing mixed with target reflections. Recently, the Pareto distribution has been related to sea clutter measurements suggesting that it may provide a better fit than other traditional distributions. The authors propose a new method for estimating the Pareto shape parameter based on art...

Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are distributed across the nodes of the network. We propose a general approach for distributed learning based on combining local estimators defined by pseudo-likelihood components, encompassing a number of combination methods, and provide both theoretical and experimental ana...