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Symmetry
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Computer Vision, Pattern Recognition, Machine Learning, and Symmetry, 2nd Edition
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Computer Vision, Pattern Recognition, Machine Learning, and Symmetry, 2nd Edition

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 4288

Special Issue Editors

Dr. Longfei Zhang

E-Mail Website
Guest Editor
Key Laboratory of Digital Performance and Simulation Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: multimedia retrieval; computer vision; machine learning; digital performance
Special Issues, Collections and Topics in MDPI journals
Dr. William Steingartner

E-Mail Website
Guest Editor
Department of computers and informatics, Technical University of Košice, 040 01 Košice, Slovakia
Interests: semantics of programming languages; software engineering; formal methods in software engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite you to submit your work to this Special Issue, "Computer Vision, Pattern Recognition, Machine Learning, and Symmetry", on the topic of symmetry/asymmetry. This Special Issue seeks high-quality contributions in the fields of computer vision/pattern recognition/machine learning and symmetry in theory, as well as applications to solve practical application problems.

This Special Issue of Symmetry will collect articles on solving real-world problems by solving data- and learning-centric technologies, including computer vision, pattern recognition, and the correlation between machine learning and symmetry. We are soliciting contributions covering all related topics including, but not limited to, vision, multimedia, biometrics, behavior analysis, adversarial learning, simulation, network security, Internet of Things, and performance. The main criteria for submission are theoretical and application-centric innovative methods aimed at solving real-world problems. There is no limit on the number of pages, but the submissions must demonstrate an understanding of the theme and a contribution to the topic.

Dr. Longfei Zhang
Dr. William Steingartner
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

  • computer vision
  • applied statistics
  • pattern recognition
  • behavior analysis
  • artificial intelligence
  • machine learning
  • adversarial learning
  • reinforcement learning
  • deep learning
  • emerging technologies (telecommunications, blockchain, Internet of Things, cyber security, digital performance, smart creativity)

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Related Special Issue

Published Papers (4 papers)

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Research

27 pages, 14879 KiB  
Article
Research on AI-Driven Classification Possibilities of Ball-Burnished Regular Relief Patterns Using Mixed Symmetrical 2D Image Datasets Derived from 3D-Scanned Topography and Photo Camera
by Stoyan Dimitrov Slavov, Lyubomir Si Bao Van, Marek Vozár, Peter Gogola and Diyan Minkov Dimitrov
Symmetry 2025, 17(7), 1131; https://doi.org/10.3390/sym17071131 - 15 Jul 2025
Viewed by 204
Abstract
The present research is related to the application of artificial intelligence (AI) approaches for classifying surface textures, specifically regular reliefs patterns formed by ball burnishing operations. A two-stage methodology is employed, starting with the creation of regular reliefs (RRs) on test parts by [...] Read more.
The present research is related to the application of artificial intelligence (AI) approaches for classifying surface textures, specifically regular reliefs patterns formed by ball burnishing operations. A two-stage methodology is employed, starting with the creation of regular reliefs (RRs) on test parts by ball burnishing, followed by 3D topography scanning with Alicona device and data preprocessing with Gwyddion, and Blender software, where the acquired 3D topographies are converted into a set of 2D images, using various virtual camera movements and lighting to simulate the symmetrical fluctuations around the tool-path of the real camera. Four pre-trained convolutional neural networks (DenseNet121, EfficientNetB0, MobileNetV2, and VGG16) are used as a base for transfer learning and tested for their generalization performance on different combinations of synthetic and real image datasets. The models were evaluated by using confusion matrices and four additional metrics. The results show that the pretrained VGG16 model generalizes the best regular reliefs textures (96%), in comparison with the other models, if it is subjected to transfer learning via feature extraction, using mixed dataset, which consist of 34,037 images in following proportions: non-textured synthetic (87%), textured synthetic (8%), and real captured (5%) images of such a regular relief. Full article
(This article belongs to the Special Issue Computer Vision, Pattern Recognition, Machine Learning, and Symmetry, 2nd Edition)
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23 pages, 5304 KiB  
Article
Improvement and Optimization of Underwater Image Target Detection Accuracy Based on YOLOv8
by Yisong Sun, Wei Chen, Qixin Wang, Tianzhong Fang and Xinyi Liu
Symmetry 2025, 17(7), 1102; https://doi.org/10.3390/sym17071102 - 9 Jul 2025
Viewed by 316
Abstract
The ocean encompasses the majority of the Earth’s surface and harbors substantial energy resources. Nevertheless, the intricate and asymmetrically distributed underwater environment renders existing target detection performance inadequate. This paper presents an enhanced YOLOv8s approach for underwater robot object detection to address issues [...] Read more.
The ocean encompasses the majority of the Earth’s surface and harbors substantial energy resources. Nevertheless, the intricate and asymmetrically distributed underwater environment renders existing target detection performance inadequate. This paper presents an enhanced YOLOv8s approach for underwater robot object detection to address issues of subpar image quality and low recognition accuracy. The precise measures are enumerated as follows: initially, to address the issue of model parameters, we optimized the ninth convolutional layer by substituting certain conventional convolutions with adaptive deformable convolution DCN v4. This modification aims to more effectively capture the deformation and intricate features of underwater targets, while simultaneously decreasing the parameter count and enhancing the model’s ability to manage the deformation challenges presented by underwater images. Furthermore, the Triplet Attention module is implemented to augment the model’s capacity for detecting multi-scale targets. The integration of low-level superficial features with high-level semantic features enhances the feature expression capability. The original CIoU loss function was ultimately substituted with Shape IoU, enhancing the model’s performance. In the underwater robot grasping experiment, the system shows particular robustness in handling radial symmetry in marine organisms and reflection symmetry in artificial structures. The enhanced algorithm attained a mean Average Precision (mAP) of 87.6%, surpassing the original YOLOv8s model by 3.4%, resulting in a marked enhancement of the object detection model’s performance and fulfilling the real-time detection criteria for underwater robots. Full article
(This article belongs to the Special Issue Computer Vision, Pattern Recognition, Machine Learning, and Symmetry, 2nd Edition)
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21 pages, 8372 KiB  
Article
Audio-Visual Learning for Multimodal Emotion Recognition
by Siyu Fan, Jianan Jing and Chongwen Wang
Symmetry 2025, 17(3), 418; https://doi.org/10.3390/sym17030418 - 11 Mar 2025
Viewed by 2318
Abstract
Most current emotion recognition methods are often limited to a single- or dual-modality approach, neglecting the rich information embedded in other modalities. This limitation hampers the accurate identification of complex or subtle emotional expressions. Additionally, to reduce the computational cost during inference, minimizing [...] Read more.
Most current emotion recognition methods are often limited to a single- or dual-modality approach, neglecting the rich information embedded in other modalities. This limitation hampers the accurate identification of complex or subtle emotional expressions. Additionally, to reduce the computational cost during inference, minimizing the model’s parameter size is essential. To address these challenges, we utilize the concept of symmetry to design a balanced multimodal architecture that integrates facial expressions, speech, and body posture information, aiming to enhance both recognition performance and computational efficiency. By leveraging the E-Branchformer network and using the F1- score as the primary performance evaluation metric, the experiments are mainly conducted on the CREMA-D corpora. The experimental results demonstrate that the proposed model outperforms baseline models on the CREMA-D dataset and an extended dataset incorporating eNTERFACE’05, achieving significant performance improvements while reducing the number of parameters. These findings demonstrate the effectiveness of the proposed approach and provide a new technical solution for the field of emotion recognition. Full article
(This article belongs to the Special Issue Computer Vision, Pattern Recognition, Machine Learning, and Symmetry, 2nd Edition)
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34 pages, 8852 KiB  
Article
A Biologically Inspired Model for Detecting Object Motion Direction in Stereoscopic Vision
by Yuxiao Hua, Sichen Tao, Yuki Todo, Tianqi Chen, Zhiyu Qiu and Zheng Tang
Symmetry 2025, 17(2), 162; https://doi.org/10.3390/sym17020162 - 22 Jan 2025
Viewed by 931
Abstract
This paper presents a biologically inspired model, the Stereoscopic Direction Detection Mechanism (SDDM), designed to detect motion direction in three-dimensional space. The model addresses two key challenges: the lack of biological interpretability in current deep learning models and the limited exploration of binocular [...] Read more.
This paper presents a biologically inspired model, the Stereoscopic Direction Detection Mechanism (SDDM), designed to detect motion direction in three-dimensional space. The model addresses two key challenges: the lack of biological interpretability in current deep learning models and the limited exploration of binocular functionality in existing biologically inspired models. Rooted in the fundamental concept of ’disparity’, the SDDM is structurally divided into components representing the left and right eyes. Each component mimics the layered architecture of the human visual system, from the retinal layer to the primary visual cortex. By replicating the functions of various cells involved in stereoscopic motion direction detection, the SDDM offers enhanced biological plausibility and interpretability. Extensive experiments were conducted to evaluate the model’s detection accuracy for various objects and its robustness against different types of noise. Additionally, to ascertain whether the SDDM matches the performance of established deep learning models in the field of three-dimensional motion direction detection, its performance was benchmarked against EfficientNet and ResNet under identical conditions. The results demonstrate that the SDDM not only exhibits strong performance and robust biological interpretability but also requires significantly lower hardware and time costs compared to advanced deep learning models. Full article
(This article belongs to the Special Issue Computer Vision, Pattern Recognition, Machine Learning, and Symmetry, 2nd Edition)
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