Results 271 to 280 of about 2,848,958 (338)

Joint blur kernel estimation and CNN for blind image restoration

Neurocomputing, 2020
Convolutional neural networks (CNN) have shown its excellent performance in computer vision fields. Recently, they are successfully applied to image restoration.
Liqin Huang, Youshen Xia
semanticscholar   +1 more source

State of charge estimation for lithium-ion battery based on Gaussian process regression with deep recurrent kernel

International Journal of Electrical Power & Energy Systems, 2021
Accurate and robust state of charge estimation of lithium-ion battery is a challenging task in battery management system. In this paper, a novel data-driven SOC estimation approach for Lithium-ion (Li-ion) batteries is proposed based on the Gaussian ...
Fei Xiao, Chaoran Li, Yaxiang Fan, Guorun Yang, Xin Tang   +4 more
semanticscholar   +1 more source

Fast & Accurate Gaussian Kernel Density Estimation

Visual .., 2021
Kernel density estimation (KDE) models a discrete sample of data as a continuous distribution, supporting the construction of visualizations such as violin plots, heatmaps, and contour plots.
Jeffrey Heer, ExtBox Deriche
semanticscholar   +1 more source

Space-variant blur kernel estimation and image deblurring through kernel clustering

Signal processing. Image communication, 2019
This paper presents a space-variant blur kernel estimation and image deblurring framework. For space-variant blur kernel estimation, the input image is divided into small patches, and for each patch, the blur kernel is estimated.
M. Z. Alam, Qinchun Qian, B. Gunturk
semanticscholar   +1 more source

Tutorial on kernel estimation of continuous spatial and spatiotemporal relative risk

Statistics in Medicine, 2017
Kernel smoothing is a highly flexible and popular approach for estimation of probability density and intensity functions of continuous spatial data. In this role, it also forms an integral part of estimation of functionals such as the density‐ratio or ...
T. Davies, Jonathan C. Marshall, M. Hazelton   +2 more
semanticscholar   +1 more source

Self-Paced Kernel Estimation for Robust Blind Image Deblurring

IEEE International Conference on Computer Vision, 2017
The challenge in blind image deblurring is to remove the effects of blur with limited prior information about the nature of the blur process. Existing methods often assume that the blur image is produced by linear convolution with additive Gaussian noise.
Dong Gong, Mingkui Tan, Yanning Zhang, Anton van den Hengel, Javen Qinfeng Shi   +4 more
semanticscholar   +1 more source

Generalized Kernel Density Estimator

Theory of Probability & Its Applications, 2000
Summary: We introduce a new class of nonparametric density estimators. It includes the classical kernel density estimators as well as the popular Abramson's estimator. We show that the generalized estimators may perform much better than the classical one if the distribution has a heavy tail.
openaire   +1 more source

Online Discriminative Kernel Density Estimator With Gaussian Kernels

IEEE Transactions on Cybernetics, 2014
We propose a new method for a supervised online estimation of probabilistic discriminative models for classification tasks. The method estimates the class distributions from a stream of data in the form of Gaussian mixture models (GMMs). The reconstructive updates of the distributions are based on the recently proposed online kernel density estimator ...
Matej, Kristan, Ales, Leonardis
openaire   +2 more sources

VARIABLE KERNEL DENSITY ESTIMATES AND VARIABLE KERNEL DENSITY ESTIMATES

Australian Journal of Statistics, 1990
SummaryThe term “variable kernel density estimate” is sometimes used to mean a kernel density estimate employing a different bandwidth for each data point, and sometimes to denote a kernel density estimate with bandwidth a function of estimation location.
openaire   +1 more source

Bootstrapping kernel spectral density estimates with kernel bandwidth estimation

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004
We address the problem of confidence interval estimation of spectral densities using the bootstrap. Of special interest is the choice of the kernel global bandwidth. First, we investigate resampling based techniques for the choice of the bandwidth.
openaire   +1 more source