Regional Variance for Multi-Object Filtering

Multi-object filtering for space situational awareness

Variance Reduced Ensemble Kalman Filtering

A number of algorithms to solve large-scale Kalman filtering problems have been introduced recently. The ensemble Kalman filter represents the probability density of the state estimate by a finite number of randomly generated system states. Another algorithm uses a singular value decomposition to select the leading eigenvectors of the covariance matrix of the state estimate and to approximate t...

Sampled-data Minimum Variance Filtering

This paper deals with the optimal solution to the sampled-data minimum variance filtering problem for linear systems with noise in the states and in the measurements. The solution is derived in the time-domain by using a fast sampling zeroorder hold input discretization of the continuous time systems together with a lifting technique. The original sampled-data system is transformed into an equi...

Interactive Multi-object Segmentation with Max-tree Filtering

This thesis presents a novel multi-object segmentation approach for volumetric grayscale data that separates the segmentation process into an unattended preprocessing phase and an interactive exploration phase. The first produces a set of feature candidates, which the user then filters repeatedly until only the desired features remain. The interactive, visualization-driven exploration completel...

On l1 mean and variance filtering

This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumption is that the mean value is piecewise constant in time, and the task is to ...