In the defense sector, artificial intelligence (AI) and machine learning (ML) have been used
to analyse and decipher massive volumes of data, namely for target recognition …

In recent years, with the great success of deep learning and especially deep unsupervised
learning, many deep architectural clustering methods, collectively known as deep clustering …

Abstract 3D edge features, which represent the boundaries between different objects or
surfaces in a 3D scene, are crucial for many computer vision tasks, including object …

Spectral clustering and embedding are critical in data mining and rely heavily on pre-
computed graph structure quality. Some methods simultaneously learn graph structures and …

Multiview clustering (MVC) has gained significant attention as it enables the partitioning of
samples into their respective categories through unsupervised learning. However, there are …

Recently there is a growing focus on graph data, and multi-view graph clustering has
become a popular area of research interest. Most of the existing methods are only …

Deep clustering has been widely applicated in various fields, including natural image and
language processing. However, when it is applied to hyperspectral image (HSI) processing …

The application of clustering has always been an important method for problem-solving. In
the era of big data, most classical clustering methods suffer from the curse of dimensionality …

Color quantization reduces the number of colors used in an image while preserving its
content, which is essential in pixel art and knitting art creation. Traditional methods primarily …

Objective. We propose a single-ended readout, multi-resolution detector design that can
achieve high spatial, depth-of-interaction (DOI), and time-of-flight (TOF) resolutions, as well …