The problem considered in this paper is one of extracting perceptually relevant information from groups of objects based on their descriptions. Object descriptions are qualitatively represented by feature-value vectors ...

The problem presented in this paper is how to find similarities between digital images useful in design cybernetic vision systems. The solution to this problem stems from a neighbourhood based vision system. A neighbourhood ...

In this thesis, we present an approach to automating the creation of land use and land cover (LULC) maps from satellite images using deep neural networks that were developed to perform semantic segmentation of natural ...

In this article, we present an approach to land-use and land-cover (LULC) mapping from multispectral satellite images using deep learning methods. The terms satellite image classification and map production, although used ...

This article presents an approach to automating the creation of land-use/land-cover classification (LULC) maps from satellite images using deep neural networks that were developed to perform semantic segmentation of natural ...

Cybernetic vision systems can be deployed in problem domains where the goal is to achieve results similar to those produced by humans. Fundamentally, these problems consist of evaluation of image content between sets of ...

This paper presents a signature-based approach to quantifying perceptual nearness of images. A signature is defined as a set of descriptors, where each descriptor consists of a real-valued feature vector associated with a ...

Purpose: The purpose of this paper is to present near set theory using the perceptual indiscernibility and tolerance relations, to demonstrate the practical application of near set theory to the image correspondence problem, ...

A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models ...