This paper presents a novel approach to particle filtering which minimizes the total tracking distortion by considering dynamic variance of proposal density and adaptive number o...
We present a multi modal sequential importance resampling particle filter algorithm for object tracking. We consider a hidden state sequence linked to several observation sequence...
Particle filters (PF) and auxiliary particle filters (APF) are widely used sequential Monte Carlo (SMC) techniques. In this paper we comparatively analyse the Sampling Importanc...
— We study the problem of optimal estimation using quantized innovations, with application to distributed estimation over sensor networks. We show that the state probability dens...
In this paper we propose a Particle Filter-based propagation approach for the segmentation of vascular structures in 3D volumes. Because of pathologies and inhomogeneities, many de...