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Electronics
Special Issues
Emerging Applications of Computer Vision Technology
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Emerging Applications of Computer Vision Technology

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 7186

Special Issue Editors

Dr. Gary A. Atkinson

E-Mail Website
Guest Editor
Centre for Machine Vision, Bristol Robotics Laboratory, Department of Engineering Design and Mathematics, Faculty of Environment and Technology, University of the West of England, Bristol, UK
Interests: computer vision; artificial neural networks; scene understanding
Dr. Mark F. Hansen

E-Mail Website
Guest Editor
Centre for Machine Vision, Bristol Robotics Laboratory, Department of Engineering Design and Mathematics, Faculty of Environment and Technology, University of the West of England, Bristol, UK
Interests: computer vision; artificial neural networks; scene understanding

Special Issue Information

Dear Colleagues,

The last two decades have seen the transformation of computer vision from a fledgling scientific discipline with just highly specialised applications into a ubiquitous source of new technologies that are starting to transform our everyday lives. Much of this recent explosion of developments and application areas is due to the powerful capabilities of convolutional neural networks (CNNs). Another key reason is the vastly reduced cost and better accuracy of novel sensing hardware, such as hyperspectral technologies, high-speed/high-precision 3D data capture, and cameras sensitive to polarised light. The combined advances in CNNs and hardware afford a step change in application areas of computer vision.

The purpose of this Special Issue is to disseminate research papers or state-of-the-art surveys that pertain to novel or emerging applications in the field of computer vision. Papers may contribute to application areas that have emerged during the past decade or may relate to new subdomains of long-standing applications. Submissions are particularly welcome in, though not limited to, the areas in the list of keywords below.

Dr. Gary A. Atkinson
Dr. Mark F. Hansen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Novel industrial applications of computer vision
  • CCTV, security
  • Computer vision in agriculture
  • Images and image analysis in entertainment
  • High-speed computer vision
  • Novel and emerging sensing technologies
  • Hyperspectral and multispectral imaging
  • Novel applications for 3D vision
  • Applications of image synthesis, generative adversarial networks
  • Computer vision in robotics, reinforcement learning

Published Papers (2 papers)

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Research

22 pages, 30086 KiB  
Article
A Smart and Robust Automatic Inspection of Printed Labels Using an Image Hashing Technique
by Mehshan Ahmed Khan, Fawad Ahmed, Muhammad Danial Khan, Jawad Ahmad, Harish Kumar and Nikolaos Pitropakis
Electronics 2022, 11(6), 955; https://doi.org/10.3390/electronics11060955 - 19 Mar 2022
Cited by 4 | Viewed by 2242
Abstract
This work is focused on the development of a smart and automatic inspection system for printed labels. This is a challenging problem to solve since the collected labels are typically subjected to a variety of geometric and non-geometric distortions. Even though these distortions [...] Read more.
This work is focused on the development of a smart and automatic inspection system for printed labels. This is a challenging problem to solve since the collected labels are typically subjected to a variety of geometric and non-geometric distortions. Even though these distortions do not affect the content of a label, they have a substantial impact on the pixel value of the label image. Second, the faulty area may be extremely small as compared to the overall size of the labelling system. A further necessity is the ability to locate and isolate faults. To overcome this issue, a robust image hashing approach for the detection of erroneous labels has been developed. Image hashing techniques are generally used in image authentication, social event detection and image copy detection. Most of the image hashing methods are computationally extensive and also misjudge the images processed through the geometric transformation. In this paper, we present a novel idea to detect the faults in labels by incorporating image hashing along with the traditional computer vision algorithms to reduce the processing time. It is possible to apply Speeded Up Robust Features (SURF) to acquire alignment parameters so that the scheme is resistant to geometric and other distortions. The statistical mean is employed to generate the hash value. Even though this feature is quite simple, it has been found to be extremely effective in terms of computing complexity and the precision with which faults are detected, as proven by the experimental findings. Experimental results show that the proposed technique achieved an accuracy of 90.12%. Full article
(This article belongs to the Special Issue Emerging Applications of Computer Vision Technology)
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26 pages, 8951 KiB  
Article
Precision Fibre Angle Inspection for Carbon Fibre Composite Structures Using Polarisation Vision
by Gary A. Atkinson, Sean O’Hara Nash and Lyndon N. Smith
Electronics 2021, 10(22), 2765; https://doi.org/10.3390/electronics10222765 - 12 Nov 2021
Cited by 9 | Viewed by 3476
Abstract
This paper evaluates the precision of polarisation imaging technology for the inspection of carbon fibre composite components. Specifically, it assesses the feasibility of the technology for fibre orientation measurements based on the premise that light is polarised by reflection from such anisotropically conductive [...] Read more.
This paper evaluates the precision of polarisation imaging technology for the inspection of carbon fibre composite components. Specifically, it assesses the feasibility of the technology for fibre orientation measurements based on the premise that light is polarised by reflection from such anisotropically conductive surfaces. A recently commercialised Sony IMX250MZR sensor is used for data capture by using various lighting conditions. The paper shows that it is possible to obtain sub-degree accuracy for cured and dry woven and unidirectional materials in ideal conditions, which comprised dark field illumination. Indeed, in ideal conditions, the average relative angles can be measured to an accuracy of 0.1–0.2°. The results also demonstrate a precision of the order 1° for more general illumination, such as dome illumination and ambient lighting, for certain material type/lens combinations. However, it is also shown that the precision varies considerably depending on illumination, lens choice and material type, with some results having errors above 2°. Finally, a feasibility study into the inspection of three-dimensional components suggests that only limited application is possible for non-planar regions without further research. Nevertheless, the observed phenomena for such components are, at least, qualitatively understood based on physics theory. Full article
(This article belongs to the Special Issue Emerging Applications of Computer Vision Technology)
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