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Symmetry
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Symmetry/Asymmetry in Image Processing and Computer Vision Using Embedded Systems
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Symmetry/Asymmetry in Image Processing and Computer Vision Using Embedded Systems

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 949

Special Issue Editors

Dr. Felipe Trujillo-Romero

E-Mail Website
Guest Editor
Departamento de Ingeniería Electrónica, Universidad de Guanajuato, Salamanca 36787, Mexico
Interests: computer vision; mobile robotics; evolutionary algorithms; embedded systems; bio-inspired algorithms
Prof. Dr. Santiago Omar Caballero-Morales

E-Mail Website
Guest Editor
School Engineering, Universidad Popular Autonoma del Estado de Puebla–UPAEP University, Puebla 72410, Mexico
Interests: pattern recognition; combinatorial optimization; logistics and supply chain management; design of distribution networks; strategic planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue examines the essential functions of symmetry and asymmetry in image processing and computer vision, especially in relation to embedded systems. It seeks to compile pioneering research that demonstrates how these notions might improve algorithm efficiency, accuracy, and performance in real-time applications.

Contributions may encompass theoretical frameworks, algorithmic innovations, and practical applications that utilize symmetry for tasks including object identification, image segmentation, and feature extraction. Conversely, research examining the advantages of asymmetrical strategies in dynamic and complex situations is also advocated.

For this reason, this Special Issue encourages researchers and practitioners from different fields to interact, focusing on how hardware and software solutions can work together in embedded systems.

We invite contributions that address a wide range of topics, including, but not limited to, the following:

  • Symmetry detection algorithms: Innovative methods for detecting and leveraging symmetrical features in images to improve recognition and classification tasks.
  • Asymmetry analysis: Approaches to analyze asymmetrical patterns and their implications for object detection and scene understanding.
  • Embedded system architectures: Designs and implementations of hardware and software frameworks that optimize the processing of symmetrical and asymmetrical image data.
  • Applications: Case studies showcasing practical applications in areas such as robotics, surveillance, medical imaging, and autonomous vehicles, highlighting how symmetry/asymmetry influences system performance.
  • Machine learning techniques: The integration of machine learning methods that utilize symmetry and asymmetry for enhanced image analysis and feature extraction.

Dr. Felipe Trujillo-Romero
Prof. Dr. Santiago Omar Caballero-Morales
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
  • embedded systems
  • object recognition
  • image segmentation
  • feature extraction
  • dynamic environments
  • hardware integration
  • real-time applications
  • machine learning

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

Published Papers (2 papers)

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21 pages, 5616 KiB  
Article
Symmetry-Guided Dual-Branch Network with Adaptive Feature Fusion and Edge-Aware Attention for Image Tampering Localization
by Zhenxiang He, Le Li and Hanbin Wang
Symmetry 2025, 17(7), 1150; https://doi.org/10.3390/sym17071150 - 18 Jul 2025
Viewed by 125
Abstract
When faced with diverse types of image tampering and image quality degradation in real-world scenarios, traditional image tampering localization methods often struggle to balance boundary accuracy and robustness. To address these issues, this paper proposes a symmetric guided dual-branch image tampering localization network—FENet [...] Read more.
When faced with diverse types of image tampering and image quality degradation in real-world scenarios, traditional image tampering localization methods often struggle to balance boundary accuracy and robustness. To address these issues, this paper proposes a symmetric guided dual-branch image tampering localization network—FENet (Fusion-Enhanced Network)—that integrates adaptive feature fusion and edge attention mechanisms. This method is based on a structurally symmetric dual-branch architecture, which extracts RGB semantic features and SRM noise residual information to comprehensively capture the fine-grained differences in tampered regions at the visual and statistical levels. To effectively fuse different features, this paper designs a self-calibrating fusion module (SCF), which introduces a content-aware dynamic weighting mechanism to adaptively adjust the importance of different feature branches, thereby enhancing the discriminative power and expressiveness of the fused features. Furthermore, considering that image tampering often involves abnormal changes in edge structures, we further propose an edge-aware coordinate attention mechanism (ECAM). By jointly modeling spatial position information and edge-guided information, the model is guided to focus more precisely on potential tampering boundaries, thereby enhancing its boundary detection and localization capabilities. Experiments on public datasets such as Columbia, CASIA, and NIST16 demonstrate that FENet achieves significantly better results than existing methods. We also analyze the model’s performance under various image quality conditions, such as JPEG compression and Gaussian blur, demonstrating its robustness in real-world scenarios. Experiments in Facebook, Weibo, and WeChat scenarios show that our method achieves average F1 scores that are 2.8%, 3%, and 5.6% higher than those of existing state-of-the-art methods, respectively. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision Using Embedded Systems)
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17 pages, 9400 KiB  
Article
MRCA-UNet: A Multiscale Recombined Channel Attention U-Net Model for Medical Image Segmentation
by Lei Liu, Xiang Li, Shuai Wang, Jun Wang and Silas N. Melo
Symmetry 2025, 17(6), 892; https://doi.org/10.3390/sym17060892 - 6 Jun 2025
Viewed by 483
Abstract
Deep learning techniques play a crucial role in medical image segmentation for diagnostic purposes, with traditional convolutional neural networks (CNNs) and emerging transformers having achieved satisfactory results. CNN-based methods focus on extracting the local features of an image, which are beneficial for handling [...] Read more.
Deep learning techniques play a crucial role in medical image segmentation for diagnostic purposes, with traditional convolutional neural networks (CNNs) and emerging transformers having achieved satisfactory results. CNN-based methods focus on extracting the local features of an image, which are beneficial for handling image details and textural features. However, the receptive fields of CNNs are relatively small, resulting in poor performance when processing images with long-range dependencies. Conversely, transformer-based methods are effective in handling global information; however, they suffer from significant computational complexity arising from the building of long-range dependencies. Additionally, they lack the ability to perceive image details and adopt channel features. These problems can result in unclear image segmentation and blurred boundaries. Accordingly, in this study, a multiscale recombined channel attention (MRCA) module is proposed, which can simultaneously extract both global and local features and has the capability of exploring channel features during feature fusion. Specifically, the proposed MRCA first employs multibranch extraction of image features and performs operations such as blocking, shifting, and aggregating the image at different scales. This step enables the model to recognize multiscale information locally and globally. Feature selection is then performed to enhance the predictive capability of the model. Finally, features from different branches are connected and recombined across channels to complete the feature fusion. Benefiting from fully exploring the channel features, an MRCA-based U-Net (MRCA-UNet) framework is proposed for medical image segmentation. Experiments conducted on the Synapse multi-organ segmentation (Synapse) dataset and the International Skin Imaging Collaboration (ISIC-2018) dataset demonstrate the competitive segmentation performance of the proposed MRCA-UNet, achieving an average Dice Similarity Coefficient (DSC) of 81.61% and a Hausdorff Distance (HD) of 23.36 on Synapse and an Accuracy of 95.94% on ISIC-2018. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision Using Embedded Systems)
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