Small object detection (SOD) in optical images and videos is a challenging problem that
even state-of-the-art generic object detection methods fail to accurately localize and identify …

Transformers have rapidly gained popularity in computer vision, especially in the field of
object recognition and detection. Upon examining the outcomes of state-of-the-art object …

In this article, the problem of estimating and tracking a subspace signal in the presence of
non-Gaussian noise is addressed. In contrast to non-robust methods such as PAST, NIC …

Belief functions theory (BFT) plays a critical role in addressing uncertainty and imprecision in
multi-source data fusion. Unfortunately, the application of Dempster's rule in BFT often …

In this paper, novel robust principal component analysis (RPCA) methods are proposed to
exploit the local structure of datasets. The proposed methods are derived by minimizing the …

Camera pose estimation has long relied on geometry-based approaches and sparse 2D-3D
keypoint correspondences. With the advent of deep learning methods, the estimation of …

Dictionary learning methods have been extensively used in different types of image and
signal processing tasks. In a number of applications, the collected data/signal may have a …

In this paper, we generalize the well-known Expectation Maximization (EM) algorithm using
the α− divergence for Gaussian Mixture Model (GMM). This approach is used in robust …

Objective: In this paper, we aim to address the problem of subspace detection in the
presence of locally-correlated complex Gaussian noise and interference. For applications …

In information theory—as well as in the adjacent fields of statistics, machine learning,
artificial intelligence, signal processing and pattern recognition—many flexibilizations of the …