Remote sensing image scene classification with deep learning (DL) is a rapidly growing
field that has gained significant attention in the past few years. While previous review papers …

Recent studies show that Vision Transformers (ViTs) exhibit strong robustness against
various corruptions. Although this property is partly attributed to the self-attention …

Despite extensive research conducted in machine vision for harvesting robots, practical
success in this field of agrobotics is still limited. This article presents a comprehensive …

This paper investigates two fundamental problems in computer vision: contour detection and
image segmentation. We present state-of-the-art algorithms for both of these tasks. Our …

Many real-world problems deal with collections of high-dimensional data, such as images,
videos, text, and web documents, DNA microarray data, and more. Often, such high …

We present a novel deep neural network architecture for unsupervised subspace clustering.
This architecture is built upon deep auto-encoders, which non-linearly map the input data …

The usage of convolutional neural networks (CNNs) for unsupervised image segmentation
was investigated in this study. Similar to supervised image segmentation, the proposed CNN …

Over the past few decades, significant progress has been made in clustering high-
dimensional data sets distributed around a collection of linear and affine subspaces. This …

We consider the problem of fitting a union of subspaces to a collection of data points drawn
from one or more subspaces and corrupted by noise and/or gross errors. We pose this …

Segmentation is a crucial phase in image processing because it simplifies the
representation of an image and facilitates its analysis. The multilevel thresholding method is …