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» Sequential Bayesian kernel modelling with non-Gaussian noise
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NN
2008
Springer
107views Neural Networks» more  NN 2008»
13 years 4 months ago
Sequential Bayesian kernel modelling with non-Gaussian noise
Nikolay Y. Nikolaev, Lilian M. de Menezes
ICASSP
2011
IEEE
12 years 8 months ago
Joint Bayesian removal of impulse and background noise
We present a method for the removal of noise including nonGaussian impulses from a signal. Impulse noise is removed jointly a homogenous Gaussian noise floor using a Gabor regres...
James Murphy, Simon J. Godsill
CVPR
2004
IEEE
14 years 6 months ago
Incremental Density Approximation and Kernel-Based Bayesian Filtering for Object Tracking
Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The pa...
Bohyung Han, Dorin Comaniciu, Ying Zhu, Larry S. D...
TSP
2008
99views more  TSP 2008»
13 years 4 months ago
Adaptive Polarized Waveform Design for Target Tracking Based on Sequential Bayesian Inference
Abstract--In this paper, we develop an adaptive waveform design method for target tracking under a framework of sequential Bayesian inference. We employ polarization diversity to i...
Martin Hurtado, Tong Zhao, Arye Nehorai
TIP
2010
141views more  TIP 2010»
12 years 11 months ago
Efficient Particle Filtering via Sparse Kernel Density Estimation
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...
Amit Banerjee, Philippe Burlina