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