Conventional estimation methods applied to Rician distributed data, (such as magnitude magnetic resonance data) yield biased results. In our work, it is shown where the bias appears. Furthermore, a novel estimation technique, based on Maximum Likelihood estimation, is developed for optimal estimation of signal as well as noise from Rician distributed data. It is shown that the proposed method i...

A method is proposed for semiparametric estimation where parametric and nonparametric criteria are exploited in density estimation and unsupervised learning. This is accomplished by making sampling assumptions on a dataset that smoothly interpolate between the extreme of independently distributed (or id) sample data (as in nonparametric kernel density estimators) to the extreme of independent i...

In this paper, we present a framework for multiterminal video compression (MTVC) that exploits the geometric constraints between cameras with overlapping fields of view, and then uses distributed source coding on corresponding points in two or more views. There are two main parts of the paper a Distributed Motion Estimation (DME) algorithm and a Distributed Source Coding (DSC) algorithm. The no...

This paper presents new algorithms for distributed statistical estimation that can take advantage of the divide-and-conquer approach. We show that one of the key benefits attained by an appropriate divide-and-conquer strategy is robustness, an important characteristic of large distributed systems. We introduce a class of algorithms that are based on the properties of the geometric median, estab...