Abstract We present two solutions for the scale selection problem in computer vision. The first one is completely nonparametric and is based on the the adaptive estimation of the normalized density gradient. Employing the sample point estimator, we define the Variable Bandwidth Mean Shift, prove its convergence, and show its superiority over the fixed bandwidth procedure. The second technique has a semiparametric nature and imposes a local structure on the data to extract reliable scale information. The local scale of the underlying density is taken as the bandwidth which maximizes the magnitude of the normalized mean shift vector. Both estimators provide practical tools for autonomous image and quasi real-time video analysis and several ex...
Scale selection methods based on local extrema over scale of scale-normalized derivatives have been ...
Variable bandwidth kernel density estimators increase the window width at low densities and decrease...
Nonparametric estimation of abrupt changes in a regression function involves choosing smoothing (ban...
The analysis of a feature space that exhibits multiscale patterns often requires kernel estimation ...
Kernel estimation techniques, such as mean shift, suffer from one major drawback: the kernel bandwid...
When estimating a mean regression function and its derivatives, locally weighted least squares regre...
The mean-shift algorithm is a robust and easy method of finding local extrema in the density distrib...
Abstract Multivariate versions of variable bandwidth kernel density estimators can lead to improveme...
Kernel estimation of a density based on contaminated data is considered and the important issue of h...
Kernel estimation techniques, such as mean shift, suffer from one major drawback: the kernel bandwid...
An inherent property of objects in the world is that they only exist as meaningful entities over cer...
International audienceIt is well established that one can improve performance of kernel density esti...
Bandwidth choice is crucial in spatial kernel estimation in exploring non-Gaussian complex spatial d...
We introduce and compare several robust procedures for bandwidth selection when estimating the vari...
The varying-coefficient model is an attractive alternative to the additive and other models. One imp...
Scale selection methods based on local extrema over scale of scale-normalized derivatives have been ...
Variable bandwidth kernel density estimators increase the window width at low densities and decrease...
Nonparametric estimation of abrupt changes in a regression function involves choosing smoothing (ban...
The analysis of a feature space that exhibits multiscale patterns often requires kernel estimation ...
Kernel estimation techniques, such as mean shift, suffer from one major drawback: the kernel bandwid...
When estimating a mean regression function and its derivatives, locally weighted least squares regre...
The mean-shift algorithm is a robust and easy method of finding local extrema in the density distrib...
Abstract Multivariate versions of variable bandwidth kernel density estimators can lead to improveme...
Kernel estimation of a density based on contaminated data is considered and the important issue of h...
Kernel estimation techniques, such as mean shift, suffer from one major drawback: the kernel bandwid...
An inherent property of objects in the world is that they only exist as meaningful entities over cer...
International audienceIt is well established that one can improve performance of kernel density esti...
Bandwidth choice is crucial in spatial kernel estimation in exploring non-Gaussian complex spatial d...
We introduce and compare several robust procedures for bandwidth selection when estimating the vari...
The varying-coefficient model is an attractive alternative to the additive and other models. One imp...
Scale selection methods based on local extrema over scale of scale-normalized derivatives have been ...
Variable bandwidth kernel density estimators increase the window width at low densities and decrease...
Nonparametric estimation of abrupt changes in a regression function involves choosing smoothing (ban...