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Symmetry
Special Issues
Symmetry and Asymmetry in Computer Vision and Graphics
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Symmetry and Asymmetry in Computer Vision and Graphics

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1986

Special Issue Editors

Prof. Dr. Yuanfeng Lian

E-Mail Website
Guest Editor
College of Artificial Intelligence, China University of Petroleum, Beijing 102249, China
Interests: image processing and virtual reality; machine vision and robotics; deep learning and digital twins
Dr. Jian-wei Liu

E-Mail Website
Guest Editor
Department of Automation, College of Artificial Intelligence, China University of Petroleum, Beijing 102249, China
Interests: analysis; prediction; controlling of complicated nonlinear system; pattern recognition and intelligence systems; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer vision and graphics have become some of the most active branches in computer science, with advances in technology allowing for the analysis and presentation of increasingly realistic three-dimensional worlds. Symmetry and asymmetry in computer vision and graphics explore the techniques of image and video analysis and understanding in computer vision, as well as the basic laws of representation, interaction, and rendering in computer graphics. This Special Issue aims to disseminate knowledge and experience while reflecting the latest developments and directions in the field of computer vision and graphics. We invite researchers, scholars, engineers, and practitioners to contribute original research articles, reviews, and case studies to this Special Issue.

Prof. Dr. Yuanfeng Lian
Dr. Jian-wei Liu
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. Symmetry 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 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

  • neural networks and deep learning
  • generative models
  • pre-training models
  • multimodal large models
  • cross-modal learning
  • object detection, tracking, and recognition
  • action recognition
  • three-dimensional reconstruction and rendering
  • computational geometry
  • shape analysis and retrieval
  • visualization and visual analysis
  • VR/AR/MR
  • digital twins
  • human–computer interaction
  • interactive machine learning
  • interactive embodied intelligence

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

24 pages, 1736 KiB  
Article
ProFusion: Multimodal Prototypical Networks for Few-Shot Learning with Feature Fusion
by Jia Zhao, Ziyang Cao, Huiling Wang, Xu Wang and Yingzhou Chen
Symmetry 2025, 17(5), 796; https://doi.org/10.3390/sym17050796 - 20 May 2025
Viewed by 68
Abstract
Existing few-shot learning models leverage vision-language pre-trained models to alleviate the data scarcity problem. However, such models usually process visual and text information separately, which causes still inherent disparities between cross-modal features. Therefore, we propose the ProFusion model, which leverages multimodal pre-trained models [...] Read more.
Existing few-shot learning models leverage vision-language pre-trained models to alleviate the data scarcity problem. However, such models usually process visual and text information separately, which causes still inherent disparities between cross-modal features. Therefore, we propose the ProFusion model, which leverages multimodal pre-trained models and prototypical networks to construct multiple prototypes. Specifically, ProFusion generates image and text prototypes symmetrically using the visual encoder and text encoder, while integrating visual and text information through the fusion module to create more expressive multimodal feature fusion prototypes. Additionally, we introduce the alignment module to ensure consistency between image and text prototypes. During inference, ProFusion calculates the similarity of test images to the three types of prototypes separately and applies a weighted sum to generate the final prediction. Experiments demonstrate that ProFusion performs outstanding classification tasks on 15 benchmark datasets. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Computer Vision and Graphics)
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16 pages, 5532 KiB  
Article
Intelligent System Study for Asymmetric Positioning of Personnel, Transport, and Equipment Monitoring in Coal Mines
by Diana Novak, Yuriy Kozhubaev, Hengbo Kang, Haodong Cheng and Roman Ershov
Symmetry 2025, 17(5), 755; https://doi.org/10.3390/sym17050755 - 14 May 2025
Viewed by 181
Abstract
The paper presents a study of an intelligent system for personnel positioning, transport, and equipment monitoring in the mining industry using convolutional neural network (CNN) and OpenPose technology. The proposed framework operates through a three-stage pipeline: OpenPose-based skeleton extraction from surveillance video streams, [...] Read more.
The paper presents a study of an intelligent system for personnel positioning, transport, and equipment monitoring in the mining industry using convolutional neural network (CNN) and OpenPose technology. The proposed framework operates through a three-stage pipeline: OpenPose-based skeleton extraction from surveillance video streams, capturing 18 key body joints at 30fps; multimodal feature fusion, combining skeletal key points and proximity sensor data to achieve environmental context awareness and obtain relevant feature values; and hierarchical pose alert, using attention-enhanced bidirectional LSTM (trained on 5000 annotated fall instances) for fall warning. The experiment conducted demonstrated that the combined use of the aforementioned technologies allows the system to determine the location and behavior of personnel, calculate the distance to hazardous areas in real time, and analyze personnel postures to identify possible risks such as falls or immobility. The system’s capacity to track the location of vehicles and equipment enhances operational efficiency, thereby mitigating the risk of accidents. Additionally, the system provides real-time alerts, identifying abnormal behavior, equipment malfunctions, and safety hazards, thus promoting enhanced mine management efficiency, improved safe working conditions, and a reduction in accidents. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Computer Vision and Graphics)
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25 pages, 11063 KiB  
Article
Evaluating the Accuracy of Smartphone-Based Photogrammetry and Videogrammetry in Facial Asymmetry Measurement
by Luiz Carlos Teixeira Coelho, Matheus Ferreira Coelho Pinho, Flávia Martinez de Carvalho, Ana Luiza Meneguci Moreira Franco, Omar C. Quispe-Enriquez, Francisco Airasca Altónaga and José Luis Lerma
Symmetry 2025, 17(3), 376; https://doi.org/10.3390/sym17030376 - 1 Mar 2025
Viewed by 1214
Abstract
Facial asymmetry presents a significant challenge for health practitioners, including physicians, dentists, and physical therapists. Manual measurements often lack the precision needed for accurate assessments, highlighting the appeal of imaging technologies like structured light scanners and photogrammetric systems. However, high-end commercial systems remain [...] Read more.
Facial asymmetry presents a significant challenge for health practitioners, including physicians, dentists, and physical therapists. Manual measurements often lack the precision needed for accurate assessments, highlighting the appeal of imaging technologies like structured light scanners and photogrammetric systems. However, high-end commercial systems remain cost prohibitive, especially for public health services in developing countries. This study aims to evaluate cell-phone-based photogrammetric methods for generating 3D facial models to detect facial asymmetries. For this purpose, 15 patients had their faces scanned with the ACADEMIA 50 3D scanner, as well as with cell phone images and videos using photogrammetry and videogrammetry, resulting in 3D facial models. Each 3D model (coming from a 3D scanner, photogrammetry, and videogrammetry) was half-mirrored to analyze dissimilarities between the two ideally symmetric face sides using Hausdorff distances between the two half-meshes. These distances were statistically analyzed through various measures and hypothesis tests. The results indicate that, in most cases, both photogrammetric and videogrammetric approaches are as reliable as 3D scanning for detecting facial asymmetries. The benefits and limitations of using images, videos, and 3D scanning are also presented. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Computer Vision and Graphics)
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