Results 231 to 240 of about 131,948 (262)

A quasi-Gaussian Kalman filter

2006 American Control Conference, 2006
In this paper, we present a Gaussian approximation to the nonlinear filtering problem, namely the quasi-Gaussian Kalman filter. Starting with the recursive Bayes filter, we invoke the Gaussian approximation to reduce the filtering problem into an optimal Kalman recursion.
Suman Chakravorty, Mrinal Kumar 0002, Puneet Singla   +2 more
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Gaussian filter for nonlinear filtering problems

Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002
We develop and analyze real-time and accurate filters for nonlinear filtering problems based on the Gaussian distributions. We present the systematic formulation of Gaussian filters and develop efficient and accurate numerical integration of the proposed filter.
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Gaussian Lifted Marginal Filtering

Proceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction, 2018
Recently, Lifted Marginal Filtering [5] has been proposed, an approach for efficient probabilistic inference in systems with multiple, (inter-)acting agents and objects (entities). The algorithm achieves its efficiency by performing inference jointly over groups of similar entities (i.e. their properties follow the same distribution). In this paper, we
Stefan Lüdtke, Alejandro Molina 0001, Thomas Kirste   +2 more
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Comments on "Gaussian particle filtering"

IEEE Transactions on Signal Processing, 2005
With the Gaussian assumption, the above paper proposed an optimal Gaussian filer under the particle filtering framework. This comment presents a different perspective from the standpoint of the conventional Gaussian filters. In this respect, the Gaussian particle filter actually extends the conventional Gaussian filter using Monte Carlo integration and
Yuanxin Wu, Xiaoping Hu 0002, Dewen Hu, Meiping Wu   +3 more
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A Gaussian Sum Filter for Unifying Gaussian and Particle Filters

State-space models (SSMs) are a broad class of probabilistic models for dynamical systems with many applications in engineering and science. Bayesian filtering is analytically tractable only in the linear-Gaussian setting, where the Kalman filter yields exact posterior distributions. For nonlinear or non-Gaussian SSMs, approximations are required.
Tsampourakis, Kostas, Elvira, Víctor
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Fast Almost-Gaussian Filtering

2010 International Conference on Digital Image Computing: Techniques and Applications, 2010
Image averaging can be performed very efficiently using either separable moving average filters or by using summed area tables, also known as integral images. Both these methods allow averaging to be performed at a small fixed cost per pixel, independent of the averaging filter size.
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Efficient approximation of Gaussian filters

IEEE Transactions on Signal Processing, 1997
This article presents improvements to the efficient approximation of Gaussian filters by sequentially applying uniform box filters. For 1-D filters, a simple and nearly optimal fit criterion for the length S of the box filters to the approximated Gaussian is given.
Richard Rau, James H. McClellan
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Gaussian sum resampling filter

2015 54th IEEE Conference on Decision and Control (CDC), 2015
In this paper we propose the Gaussian sum resampling filter (GSRF) in which the predicted state distribution is approximated by the sum of the sub-Gaussian components whose variances are designed to be smaller than the Gaussian components used for the standard Gaussian sum filter (GSF).
Masaya Murata, Hidehisa Nagano, Kunio Kashino   +2 more
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Gaussian Lifted Marginal Filtering

2019
Recently, Lifted Marginal Filtering has been proposed, an efficient Bayesian filtering algorithm for stochastic systems consisting of multiple, (inter-)acting agents and objects (entities). The algorithm achieves its efficiency by performing inference jointly over groups of similar entities (i.e. their properties follow the same distribution).
Stefan Lüdtke, Alejandro Molina 0001, Kristian Kersting, Thomas Kirste   +3 more
openaire   +1 more source

Gradient adaptive Gaussian image filter

2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015
In this study, a filter which uses the Gaussian function and selects its variance according to local properties of image without user intervention has been designed in order to eliminate the noise. Consequently, a gradient adaptive image filter that estimates variance and creates different kernel for each pixel in image has been obtained.
Kayhan Celik, Hasan Hüseyin Sayan, Recep Demirci   +2 more
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