Background modeling and foreground detection are important steps in video processing
used to detect robustly moving objects in challenging environments. This requires effective …

Background and objective Medical images are huge collections of information that are
difficult to store and process consuming extensive computing time. Therefore, the reduction …

Many approaches to image restoration are aimed at removing either Gaussian or uniform
impulsive noise. This is because both types of degradation processes are distinct in nature …

In this paper, we propose a novel method SOMKE, for kernel density estimation (KDE) over
data streams based on sequences of self-organizing map (SOM). In many stream data …

Competitive learning is an important machine learning approach which is widely employed
in artificial neural networks. In this paper, we present a rigorous definition of a new type of …

Background modeling and foreground detection are key parts of any computer vision
system. These problems have been addressed in literature with several probabilistic …

Semisupervised learning is a machine learning approach that is able to employ both labeled
and unlabeled samples in the training process. In this paper, we propose a semisupervised …

For the first time, we introduced the probabilistic principal component analysis (pPCA)
regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position …

A new confabulation-inspired association rule mining (CARM) algorithm is proposed using
an interestingness measure inspired by cogency. Cogency is only computed based on …

Many background modelling approaches are based on mixtures of multivariate Gaussians
with diagonal covariance matrices. This often yields good results, but complex backgrounds …