Background/foreground separation is the first step in video surveillance system to detect
moving objects. Recent research on problem formulations based on decomposition into low …

Background modeling has emerged as a popular foreground detection technique for various
applications in video surveillance. Background modeling methods have become increasing …

We present a survey on maritime object detection and tracking approaches, which are
essential for the development of a navigational system for autonomous ships. The electro …

Sparse estimation methods are aimed at using or obtaining parsimonious representations of
data or models. They were first dedicated to linear variable selection but numerous …

Compressed sensing (CS) is an emerging field that has attracted considerable research
interest over the past few years. Previous review articles in CS limit their scope to standard …

Principal component analysis is a fundamental operation in computational data analysis,
with myriad applications ranging from web search to bioinformatics to computer vision and …

Object detection is a fundamental step for automated video analysis in many vision
applications. Object detection in a video is usually performed by object detectors or …

We address single image super-resolution using a statistical prediction model based on
sparse representations of low-and high-resolution image patches. The suggested model …

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition
of sparse or compressible signals that can be well approximated by just K¿ N elements from …

Submodular functions are relevant to machine learning for at least two reasons:(1) some
problems may be expressed directly as the optimization of submodular functions and (2) the …