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J. Imaging
Special Issues
Augmented Vision for Industry 4.0
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Augmented Vision for Industry 4.0

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 4574

Special Issue Editors

Prof. Dr. Miguel Angel Guevara Lopez

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Guest Editor
Department of Systems and Informatics, Setubal School of Technology, Polytechnic Institute of Setubal, Campus do IPS, Estefanilha, 2910-761 Setubal, Portugal
Interests: computer vision; machine (deep) learning; data science; artificial intelligence; industry 4.0/5.0; biomedical image and data analysis; medical imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Luís Gonzaga Mendes Magalhães

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Guest Editor
Department of Information Systems (DSI), School of Engineering, University of Minho, Campus Azurém, 4800–058 Guimarães, Portugal
Interests: 3D computer graphics; virtual reality
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Raúl Ramos Pollán

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Guest Editor
Department of Computer Science, Faculty of Engineering, Universidad de Antioquia, Medellín, Colombia
Interests: deep learning; computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0 (I4.0) is an innovative paradigm aimed towards the fusion of the digital and physical world through the advent of technologies, an amalgamation of physical and digital systems that has proven to be revolutionary, and enabling the communication between physical objects and cyber infrastructures. Condition monitoring is a challenging Industry 4.0 field that features the interfaces needed for smooth information exchange between the plant control system, smart sensing tasks, process visualization unit, and operator. In this sense, the proper combination of emergent techniques, such as, computer vision, augmented and mixed reality, and artificial intelligence, are playing a key role in human–machine integration/collaboration tasks, in order to make Industry 4.0 possible, and the concept of “smart factory” a reality more each day.

The aim of this Special Issue is to present and highlight novel algorithms, methods, and applications of emergent techniques for creating augmented and intelligent vision systems for Industry 4.0, providing relevant and contextualized information to the human operators.

Prof. Dr. Miguel Angel Guevara Lopez
Prof. Dr. Luís Gonzaga Mendes Magalhães
Prof. Dr. Raúl Ramos Pollán
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. Journal of Imaging is an international peer-reviewed open access monthly 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 1800 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

  • computer vision
  • artificial intelligence
  • semantic scene understanding
  • augmented and mixed reality
  • human–machine interaction
  • condition monitoring
  • smart factory
  • Industry 4.0

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Published Papers (1 paper)

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Research

21 pages, 4784 KiB  
Article
A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces
by Paola Pierleoni, Alberto Belli, Lorenzo Palma and Luisiana Sabbatini
J. Imaging 2020, 6(6), 48; https://doi.org/10.3390/jimaging6060048 - 13 Jun 2020
Cited by 8 | Viewed by 3673
Abstract
The Industry 4.0 paradigm is based on transparency and co-operation and, hence, on monitoring and pervasive data collection. In highly standardized contexts, it is usually easy to gather data using available technologies, while, in complex environments, only very advanced and customizable technologies, such [...] Read more.
The Industry 4.0 paradigm is based on transparency and co-operation and, hence, on monitoring and pervasive data collection. In highly standardized contexts, it is usually easy to gather data using available technologies, while, in complex environments, only very advanced and customizable technologies, such as Computer Vision, are intelligent enough to perform such monitoring tasks well. By the term “complex environment”, we especially refer to those contexts where human activity which cannot be fully standardized prevails. In this work, we present a Machine Vision algorithm which is able to effectively deal with human interactions inside a framed area. By exploiting inter-frame analysis, image pre-processing, binarization, morphological operations, and blob detection, our solution is able to count the pieces assembled by an operator using a real-time video input. The solution is compared with a more advanced Machine Learning-based custom object detector, which is taken as reference. The proposed solution demonstrates a very good performance in terms of Sensitivity, Specificity, and Accuracy when tested on a real situation in an Italian manufacturing firm. The value of our solution, compared with the reference object detector, is that it requires no training and is therefore extremely flexible, requiring only minor changes to the working parameters to translate to other objects, making it appropriate for plant-wide implementation. Full article
(This article belongs to the Special Issue Augmented Vision for Industry 4.0)
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