Now showing items 1-13 of 13

    • Perceptual image analysis 

      Henry, C.; Peters, J. F. (Inderscience Enterprises Ltd., 2010)
      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 ...
    • Perception-based image classification: Framework for perception-based cybernetics 

      Henry, Christopher; Peters, James F. (Emerald Insight, 2010-08-24)
      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, ...
    • Neighbourhood-based vision systems 

      Henry, Christopher J.; Peters, James F. (Taylor and Francis, 2011)
      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 ...
    • Arthritic hand-finger movement similarity measurements: Tolerance near set approach 

      Henry, Christopher (Hindawi Publishing Corporation, 2011)
      The problem considered in this paper is how to measure the degree of resemblance between nonarthritic and arthritic hand movements during rehabilitation exercise. The solution to this problem stems from recent work on a ...
    • Neighbourhoods, classes, and near sets 

      Henry, Christopher J. (Applied Mathematical Sciences,, 2011)
      The article calls attention to the relationship between neighbourhoods and tolerance classes in the foundations of tolerance near sets. A particular form of tolerance relation is given by way of introduction to descriptively ...
    • Quantifying nearness in visual spaces 

      Henry, Christopher J.; Ramanna, Sheela; Levy, Daniel (Taylor & Francis, 2013)
      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 ...
    • Signature-based perceptual nearness: Application of near sets to image retrieval 

      Henry, Christopher J.; Ramanna, Sheela (Birkhäuser, 2013)
      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 ...
    • Metric free nearness measure using description-based neighbourhoods 

      Henry, Christopher J. (Springer, 2013-02-26)
      The focus of this paper is on a metric free nearness measure for quantifying the descriptive nearness of digital images. Regions of Interest (ROI) play an important role in discerning perceptual similarity within a single ...
    • Measuring the nearness of layered flow graphs: Application to Content Based Image Retrieval 

      Kaur, Kanwarpreet; Ramanna, Sheela; Henry, Christopher (IOS Press, 2016-03-01)
      Rough set based flow graphs represent the flow of information for a given data set where branches of these could be constructed as decision rules. However, in the recent years, the concept of flow graphs has been applied ...
    • Automated Land Use and Land Cover Map Production: A Deep Learning Framework 

      Alhassan, Victor (University of WinnipegUniversity of Winnipeg, 2018-10-19)
      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 ...
    • Automated LULC Map Production using Deep Neural Networks 

      Henry, Christopher J.; Storie, Christopher; Palaniappan, Muthu; Alhassan, Victor; Swamy, Mallikarjun; Aleshinloye, Damilola; Curtis, Andrew; Kima, Daeyoun (Taylor & Francis, 2019-01-17)
      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 ...
    • A Deep Learning Framework: Land-Use/Land-Cover Mapping and Analysis using Multispectral Satellite Imagery 

      Alhassan, Victor; Henry, Christopher; Ramanna, Sheela; Storie, Christopher (Springer, 2019-07-17)
      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 ...