Recent Advances in Metaverse and Computer Vision

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

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 9825

Special Issue Editors


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Guest Editor
Department of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia
Interests: database programming; advanced machine learning; feature selection; artificial neural networks; computer vision; object detection
Special Issues, Collections and Topics in MDPI journals

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Co-Guest Editor
Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50715, Indonesia
Interests: machine learning; application of artificial intelligence; image and vision computing; pattern recognition; augmented reality and virtual reality; recommendation systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Co-Guest Editor
Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50715, Indonesia
Interests: artificial intelligence; machine learning; metaheuristics; data analytics and intelligence system

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to the recent advances in the metaverse and computer vision. The metaverse is a highly scalable and immutable network of interconnected virtual worlds centered on real-time interactions where individuals can work, socialize, transact, play, and even create. It completely immerses the user in a virtual environment through virtualization and advanced technologies (augmented reality (AR), virtual reality (VR), etc.). Computer vision plays an important role in creating the illusion of virtual worlds in the metaverse universe. This Special Issue aims to present original, unpublished, and breakthrough research in the metaverse and computer vision focusing on new algorithms and mechanisms, such as artificial intelligence, machine learning, and explainable artificial intelligence (XAI). We aim to bring leading scientists and researchers together and create an interdisciplinary platform for the exchange of computational theories, methodologies, and techniques.

Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Artificial intelligence;
  • Machine learning and deep learning;
  • Audio/video systems and signal processing;
  • Advanced computing and data sciences;
  • Object detection and recognition systems;
  • Image processing;
  • Explainable artificial intelligent (XAI);
  • Virtual reality, augmented reality, mixed reality, and cinematic reality;
  • Embedded systems and transfer learning;
  • Image and vision computing;
  • Pattern recognition;
  • Recommendation systems;
  • Advanced technologies and applications;
  • Image/video-based object detection using deep learning;
  • Sensor fusion for object detection using deep learning;
  • Online learning for object detection;
  • Semi-supervised learning for object detection;
  • Deep-learning-based object detection for real-world applications;
  • New database for object detection;
  • Survey for the metaverse and computer vision.

We look forward to receiving your contributions.

Dr. Christine Dewi
Prof. Dr. Rung-Ching Chen
Dr. Hendry Hendry
Dr. Hindriyanto Dwi Purnomo
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

  • metaverse
  • virtual reality
  • computer vision
  • object detection
  • image processing
  • artificial intelligent
  • object recognition
  • augmented reality
  • pattern recognition
  • explainable artificial intelligent (XAI)

Published Papers (2 papers)

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Research

26 pages, 13100 KiB  
Article
Usability Analysis of a Virtual Reality Exposure Therapy Serious Game for Blood Phobia Treatment: Phobos
by João Petersen, Vítor Carvalho, João Tiago Oliveira and Eva Oliveira
Electronics 2024, 13(7), 1350; https://doi.org/10.3390/electronics13071350 - 3 Apr 2024
Viewed by 618
Abstract
Phobias are characterized as the excessive or irrational fear of an object or situation, and specific phobias affect about 10% of the world population. Blood-injection-injury phobia is a specific phobia that has a unique physical response to phobic stimuli, that is, a vasovagal [...] Read more.
Phobias are characterized as the excessive or irrational fear of an object or situation, and specific phobias affect about 10% of the world population. Blood-injection-injury phobia is a specific phobia that has a unique physical response to phobic stimuli, that is, a vasovagal syncope that causes the person to faint. Phobos is a serious game intended for blood phobia treatment that was created to be played in virtual reality with an HTC Vive that has photorealistic graphics to provide a greater immersion. We also developed a console application in C# for electrocardiography sensor connectivity and data acquisition, which gathers a 1 min baseline reading and then has continuous data acquisition during gameplay. Usability tests were conducted with self-reported questionnaires and with a case study population of 10 testers, which gave insight into the previous game experience of the tester for both digital games and virtual reality games, evaluating the discomfort for hardware on both the sensor and the virtual reality headset, as well as the game regarding usability, user experience, level of immersion, and the existence of motion sickness and its source. The results corroborate that the immersion of the game is good, which suggests that it will help with triggering the phobia. Full article
(This article belongs to the Special Issue Recent Advances in Metaverse and Computer Vision)
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19 pages, 3897 KiB  
Article
Eye Aspect Ratio for Real-Time Drowsiness Detection to Improve Driver Safety
by Christine Dewi, Rung-Ching Chen, Chun-Wei Chang, Shih-Hung Wu, Xiaoyi Jiang and Hui Yu
Electronics 2022, 11(19), 3183; https://doi.org/10.3390/electronics11193183 - 4 Oct 2022
Cited by 19 | Viewed by 7788
Abstract
Drowsiness is a major risk factor for road safety, contributing to serious injury, death, and economic loss on the road. Driving performance decreases because of increased drowsiness. In several different applications, such as facial movement analysis and driver safety, blink detection is an [...] Read more.
Drowsiness is a major risk factor for road safety, contributing to serious injury, death, and economic loss on the road. Driving performance decreases because of increased drowsiness. In several different applications, such as facial movement analysis and driver safety, blink detection is an essential requirement that is used. The extremely rapid blink rate, on the other hand, makes automatic blink detection an extremely challenging task. This research paper presents a technique for identifying eye blinks in a video series recorded by a car dashboard camera in real time. The suggested technique determines the facial landmark positions for each video frame and then extracts the vertical distance between the eyelids from the facial landmark positions. The algorithm that has been proposed estimates the facial landmark positions, extracts a single scalar quantity by making use of Eye Aspect Ratio (EAR), and identifies the eye closeness in each frame. In the end, blinks are recognized by employing the modified EAR threshold value in conjunction with a pattern of EAR values in a relatively short period of time. Experimental evidence indicates that the greater the EAR threshold, the worse the AUC’s accuracy and performance. Further, 0.18 was determined to be the optimum EAR threshold in our research. Full article
(This article belongs to the Special Issue Recent Advances in Metaverse and Computer Vision)
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