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Symmetry
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Symmetry/Asymmetry in Image Processing and Computer Vision
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Symmetry/Asymmetry in Image Processing and Computer Vision

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1805

Special Issue Editors

Dr. Xingbo Dong

E-Mail Website
Guest Editor
Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Artificial Intelligence, Anhui University, Hefei 230601, China
Interests: image processing (both medical and natural); computer vision; pattern recognition
Dr. Shuo Yang

E-Mail Website
Guest Editor
College of Computer Science, Inner Mongolia University, Hohhot 010031, China
Interests: computer vision; image processing; pattern recognition

Special Issue Information

Dear Colleagues,

This Special Issue of Symmetry focuses on the exploration of symmetry and asymmetry in image processing and computer vision, aiming to advance the understanding of these fundamental principles and their applications in modern visual technologies. Symmetry and asymmetry play crucial roles in a wide range of computer vision tasks, including object recognition, image segmentation, feature extraction, and 3D reconstruction. By investigating the impact of symmetry and asymmetry on algorithm design and performance, contributors can offer insights into how these principles can be harnessed to enhance image quality, improve computational efficiency, and achieve robust results in complex visual environments.

We invite contributions that delve into the theoretical and practical aspects of symmetry and asymmetry in image processing and computer vision, as well as innovative methods for leveraging these principles. Topics may include, but are not limited to, symmetry-based feature extraction, deep learning architectures, asymmetry handling in face and object recognition, applications of symmetry and asymmetry in medical imaging, and challenges in processing asymmetric or imperfectly symmetric data.

This Special Issue aims to serve as a collaborative platform for researchers, developers, and industry professionals, fostering the exchange of new ideas, techniques, and best practices. We hope to create a comprehensive resource that demonstrates the versatility of symmetry and asymmetry in solving critical challenges in image processing and computer vision, ultimately advancing the field and inspiring future developments.

Dr. Xingbo Dong
Dr. Shuo Yang
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

  • image processing
  • computer vision
  • pattern recognition
  • object detection
  • image segmentation
  • deep learning
  • machine learning
  • convolutional neural network

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

Published Papers (2 papers)

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25 pages, 9676 KiB  
Article
You Only Look Once–Aluminum: A Detection Model for Complex Aluminum Surface Defects Based on Improved YOLOv8
by Jiashu Han, Huiye Chen, Yitong Ding, Shudong Zhuang, Chengyu Zhou and Hua Chen
Symmetry 2025, 17(5), 724; https://doi.org/10.3390/sym17050724 - 9 May 2025
Viewed by 299
Abstract
Detecting aluminum defects in industrial environments presents significant challenges related to low-resolution images, subtle damage features, and an imbalance between easy and difficult samples. The You Only Look Once–Aluminum (YOLO-AL) algorithm proposed in this paper addresses these challenges. Firstly, to enhance the model’s [...] Read more.
Detecting aluminum defects in industrial environments presents significant challenges related to low-resolution images, subtle damage features, and an imbalance between easy and difficult samples. The You Only Look Once–Aluminum (YOLO-AL) algorithm proposed in this paper addresses these challenges. Firstly, to enhance the model’s performance on low-resolution images and small object detection, as well as to improve its flexibility and adaptability, C2f-US replaces the first two CSP bottleneck with 2 Convolutions (C2f) layers in the original Backbone network. Secondly, to boost multi-scale context capture and strip defect detection, a CPMSCA mechanism with a class-symmetric structure is proposed and integrated at the end of the Backbone network. Thirdly, to efficiently capture both high-level semantics and low-level spatial details, and improve detection of complex aluminum surface defects, ODE-RepGFPN is introduced to replace the entire Neck network. Finally, to address the imbalance between hard and easy samples, Focaler-WIoU is proposed. Extensive experiments conducted on the publicly available AliCloud dataset (APDDD) demonstrate that YOLO-AL achieves 86.5%, 77.8%, and 81.5% for Precision, Recall, and mAP@0.5, respectively, surpassing both the baseline model and other state-of-the-art methods. The model can be integrated with an industrial camera system for the automated inspection of aluminum profiles in a production environment. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision)
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15 pages, 10789 KiB  
Article
Deep Double Towers Click Through Rate Prediction Model with Multi-Head Bilinear Fusion
by Yuan Zhang, Xiaobao Cheng, Wei Wei and Yangyang Meng
Symmetry 2025, 17(2), 159; https://doi.org/10.3390/sym17020159 - 22 Jan 2025
Viewed by 1045
Abstract
The click-through rate (CTR) forecast is among the mainstream research directions in the domain of recommender systems, especially in online advertising suggestions. Among them, the multilayer perceptron (MLP) has been extensively utilized as the cornerstone of deep CTR prediction models. However, current neural [...] Read more.
The click-through rate (CTR) forecast is among the mainstream research directions in the domain of recommender systems, especially in online advertising suggestions. Among them, the multilayer perceptron (MLP) has been extensively utilized as the cornerstone of deep CTR prediction models. However, current neural network-based CTR prediction models commonly employ a single MLP network to capture nonlinear interactions between high-order features, while disregarding the interaction among differentiated features, resulting in poor model performance. Although studies such as DeepFM have proposed dual-branch interaction models to learn complex features, they still fall short of achieving more nuanced feature fusion. To address these challenges, we propose a novel model, the Deep Double Towers model (DDT), which improves the accuracy of CTR prediction through multi-head bilinear fusion while incorporating symmetry in its architecture. Specifically, the DDT model leverages symmetric parallel MLP networks to capture the interactions between differentiated features in a more structured and balanced manner. Furthermore, the multi-head bilinear fusion layer enables refined feature fusion through symmetry-aware operations, ensuring that feature interactions are aligned and symmetrically integrated. Experimental results on publicly available datasets, such as Criteo and Avazu, show that DDT surpasses existing models in improving the accuracy of CTR prediction, with symmetry contributing to more effective and balanced feature fusion. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision)
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