Kernel density estimation

Known as: Kernel density estimate, Kernel density, Parzen Windows 
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel… (More)
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Topic mentions per year

Topic mentions per year

1977-2018
05010015019772018

Papers overview

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Highly Cited
2010
Highly Cited
2010
We present a new adaptive kernel density estimator based on linear diffusion processes. The proposed estimator builds on existing… (More)
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Highly Cited
2008
Highly Cited
2008
In this paper, we propose a method for robust kernel density estimation. We interpret a KDE with Gaussian kernel as the inner… (More)
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Highly Cited
2006
Highly Cited
2006
The estimation of the underlying probability density of n i.i.d. random objects on a compact Riemannian manifold without boundary… (More)
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Highly Cited
2005
Highly Cited
2005
We propose a nonlinear statistical shape model for level set segmentation which can be efficiently implemented. Given a set of… (More)
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Highly Cited
2004
Highly Cited
2004
Background modeling is an important component of many vision systems. Existing work in the area has mostly addressed scenes that… (More)
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Highly Cited
2004
Highly Cited
2004
  • 2004
If a probability density function has bounded support, kernel density estimates often overspill the boundaries and are… (More)
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Highly Cited
2003
Highly Cited
2003
Evaluating sums of multivariate Gaussians is a common computational task in computer vision and pattern recognition, including in… (More)
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Highly Cited
2003
Highly Cited
2003
Many vision algorithms depend on the estimation of a probability density function from observations. Kernel density estimation… (More)
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2003
2003
This insert describes the module akdensity. akdensity extends the official kdensity that estimates density functions by the… (More)
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Highly Cited
2001
Highly Cited
2001
Automatic understanding of events happening at a site is the ultimate goal for many visual surveillance systems. Higher level… (More)
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