In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

Computer vision applications based on videos often require the detection of moving objects
in their first step. Background subtraction is then applied in order to separate the background …

Robust detection of infrared small and dim targets with highly heterogeneous backgrounds
plays an indispensable role in infrared search and tracking (IRST) system, which is still a …

Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

Despite the promising results, tensor robust principal component analysis (TRPCA), which
aims to recover underlying low-rank structure of clean tensor data corrupted with …

Moving object detection is a fundamental step in various computer vision applications.
Robust principal component analysis (RPCA)-based methods have often been employed for …

Purpose: This study sought to review the characteristics, strengths, weaknesses variants,
applications areas and data types applied on the various Dimension Reduction techniques …

This paper develops a new class of nonconvex regularizers for low-rank matrix recovery.
Many regularizers are motivated as convex relaxations of the\emph {matrix rank} function …

Anomaly detection in hyperspectral images (HSIs) has attracted considerable interest in the
remote-sensing domain, which aims to identify pixels with different spectral and spatial …

Detecting objects in surveillance videos is an important problem due to its wide applications
in traffic control and public security. Existing methods tend to face performance degradation …