7 search results - page 1 / 2 » Efficient Particle Filtering via Sparse Kernel Density Estim... |
TIP
2010
13 years 4 months ago
2010
Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to approp...
CVPR
2005
IEEE
14 years 11 months ago
2005
IEEE
A new particle filter, Kernel Particle Filter (KPF), is proposed for visual tracking for multiple objects in image sequences. The KPF invokes kernels to form a continuous estimate...
CVPR
2004
IEEE
14 years 11 months ago
2004
IEEE
Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The pa...
ICIP
2006
IEEE
14 years 11 months ago
2006
IEEE
We introduce a class of robust non-parametric estimation methods which are ideally suited for the reconstruction of signals and images from noise-corrupted or sparsely collected s...
CVPR
2010
IEEE
12 years 6 months ago
2010
IEEE
Robust tracking of abrupt motion is a challenging task
in computer vision due to the large motion uncertainty. In
this paper, we propose a stochastic approximation Monte
Carlo (...