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Electronics
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Application of Machine Learning in Graphics and Images, 2nd Edition
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Application of Machine Learning in Graphics and Images, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 8019

Special Issue Editors

Dr. Yiqi Wu

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: computer graphics; computer-aided design; computer vision and computer-supported cooperative work
Special Issues, Collections and Topics in MDPI journals
Dr. Yilin Chen

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China
Interests: intelligent optimization; medical image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Lu Zou

E-Mail Website
Guest Editor
School of Computer Science & Engineering, Artificial Intelligence, Wuhan Institute of Technology, Wuhan 430205, China
Interests: object pose estimation; human activity analysis; 3D object detection; point cloud processing

Special Issue Information

Dear Colleagues,

Computer graphics and image processing technologies have been widely used in production processes in society today, as well as other aspects of daily life, offering solutions with greatly improved efficiency and quality. Meanwhile, the last few decades have witnessed machine learning modes becoming effective and ubiquitous approaches applied to various challenging real-world or virtual tasks. Both the fields of image processing and computer graphics are important machine learning application scenarios that have stimulated high research interest and brought about a series of popular research directions.

In this Special Issue, we look forward to your novel research papers or comprehensive surveys of state-of-the-art works that may contribute to innovative machine learning application models, improvements to classical computer graphics and image processing tasks, and new interesting applications. Topics of interest include all aspects of the application of machine learning to graphics and images, but are not limited to the following detailed list:

  • Computer graphics;
  • Image processing;
  • Computer vision;
  • Machine learning and deep learning;
  • Pattern recognition;
  • Object detection, recognition, and tracking;
  • Part and semantic segmentation;
  • Rigid and non-rigid registration;
  • 3D reconstruction;
  • Virtual reality/augmented reality/mixed reality;
  • Computer-aided design/engineering;
  • Human pose and behavior understanding;
  • Autonomous driving.

Dr. Yiqi Wu
Dr. Yilin Chen
Dr. Lu Zou
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

  • computer graphics
  • image processing
  • computer vision
  • machine learning
  • deep learning
  • pattern recognition

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Related Special Issue

Published Papers (6 papers)

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Research

22 pages, 9103 KiB  
Article
IRST-CGSeg: Infrared Small Target Detection Based on Clustering-Guided Graph Learning and Hierarchical Features
by Guimin Jia, Tao Chen, Yu Cheng and Pengyu Lu
Electronics 2025, 14(5), 858; https://doi.org/10.3390/electronics14050858 - 21 Feb 2025
Viewed by 455
Abstract
Infrared small target detection (IRSTD) aims to segment small targets from an infrared clutter background. However, the long imaging distance, complex background, and extremely limited number of target pixels pose great challenges for IRSTD. In this paper, we propose a new IRSTD method [...] Read more.
Infrared small target detection (IRSTD) aims to segment small targets from an infrared clutter background. However, the long imaging distance, complex background, and extremely limited number of target pixels pose great challenges for IRSTD. In this paper, we propose a new IRSTD method based on the deep graph neural network to fully extract and fuse the texture and structural information of images. Firstly, a clustering algorithm is designed to divide the image into several subgraphs as a prior knowledge to guide the initialization of the graph structure of the infrared image, and the image texture features are integrated to graph construction. Then, a graph feature extraction module is designed, which guides nodes to interact with features within their subgraph via the adjacency matrix. Finally, a hierarchical graph texture feature fusion module is designed to concatenate and stack the structure and texture information at different levels to realize IRSTD. Extensive experiments have been conducted, and the experimental results demonstrate that the proposed method has high interaction over union (IoU) and probability of detection (Pd) on public datasets and the self-constructed dataset, indicating that it has fine shape segmentation and accurate positioning for infrared small targets. Full article
(This article belongs to the Special Issue Application of Machine Learning in Graphics and Images, 2nd Edition)
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18 pages, 1869 KiB  
Article
A Deepfake Image Detection Method Based on a Multi-Graph Attention Network
by Guorong Chen, Chongling Du, Yuan Yu, Hong Hu, Hongjun Duan and Huazheng Zhu
Electronics 2025, 14(3), 482; https://doi.org/10.3390/electronics14030482 - 24 Jan 2025
Viewed by 1359
Abstract
Deep forgery detection plays a crucial role in addressing the challenges posed by the rapid spread of deeply generated content that significantly erodes public trust in online information and media. Deeply forged images typically present subtle but significant artifacts in multiple regions, such [...] Read more.
Deep forgery detection plays a crucial role in addressing the challenges posed by the rapid spread of deeply generated content that significantly erodes public trust in online information and media. Deeply forged images typically present subtle but significant artifacts in multiple regions, such as in the background, lighting, and localized details. These artifacts manifest as unnatural visual distortions, inconsistent lighting, or irregularities in subtle features that break the natural coherence of the real image. To address these features of forged images, we propose a novel and efficient deep image forgery detection method that utilizes Multi-Graph Attention (MGA) techniques to extract global and local features and minimize accuracy loss. Specifically, our method introduces an interactive dual-channel encoder (DIRM), which aims to extract global and channel-specific features and facilitate complex interactions between these feature sets. In the decoding phase, one of the channels is processed as a block and combined with a Dynamic Graph Attention Network (PDGAN), which is capable of recognizing and amplifying forged traces in local information. To further enhance the model’s ability to capture global context, we propose a global Height–Width Graph Attention Module (HWGAN), which effectively extracts and associates global spatial features. Experimental results show that the classification accuracy of our method for forged images in the GenImage and CIFAKE datasets is comparable to that of the optimal benchmark method. Notably, our model achieves 97.89% accuracy on the CIFAKE dataset and has the lowest number of model parameters and lowest computational overhead. These results highlight the potential of our method for deep forgery image detection. Full article
(This article belongs to the Special Issue Application of Machine Learning in Graphics and Images, 2nd Edition)
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26 pages, 23622 KiB  
Article
CPS-RAUnet++: A Jet Axis Detection Method Based on Cross-Pseudo Supervision and Extended Unet++ Model
by Jianhong Gan, Kun Cai, Changyuan Fan, Xun Deng, Wendong Hu, Zhibin Li, Peiyang Wei, Tao Liao and Fan Zhang
Electronics 2025, 14(3), 441; https://doi.org/10.3390/electronics14030441 - 22 Jan 2025
Viewed by 660
Abstract
Atmospheric jets are pivotal components of atmospheric circulation, profoundly influencing surface weather patterns and the development of extreme weather events such as storms and cold waves. Accurate detection of the jet stream axis is indispensable for enhancing weather forecasting, monitoring climate change, and [...] Read more.
Atmospheric jets are pivotal components of atmospheric circulation, profoundly influencing surface weather patterns and the development of extreme weather events such as storms and cold waves. Accurate detection of the jet stream axis is indispensable for enhancing weather forecasting, monitoring climate change, and mitigating disasters. However, traditional methods for delineating atmospheric jets are plagued by inefficiency, substantial errors, and pronounced subjectivity, limiting their applicability in complex atmospheric scenarios. Current research on semi-supervised methods for extracting atmospheric jets remains scarce, with most approaches dependent on traditional techniques that struggle with stability and generalization. To address these limitations, this study proposes a semi-supervised jet stream axis extraction method leveraging an enhanced U-Net++ model. The approach incorporates improved residual blocks and enhanced attention gate mechanisms, seamlessly integrating these enhanced attention gates into the dense skip connections of U-Net++. Furthermore, it optimizes the consistency learning phase within semi-supervised frameworks, effectively addressing data scarcity challenges while significantly enhancing the precision of jet stream axis detection. Experimental results reveal the following: (1) With only 30% of labeled data, the proposed method achieves a precision exceeding 80% on the test set, surpassing state-of-the-art (SOTA) baselines. Compared to fully supervised U-Net and U-Net++ methods, the precision improves by 17.02% and 9.91%. (2) With labeled data proportions of 10%, 20%, and 30%, the proposed method outperforms the MT semi-supervised method, achieving precision gains of 9.44%, 15.58%, and 19.50%, while surpassing the DCT semi-supervised method with improvements of 10.24%, 16.64%, and 14.15%, respectively. Ablation studies further validate the effectiveness of the proposed method in accurately identifying the jet stream axis. The proposed method exhibits remarkable consistency, stability, and generalization capabilities, producing jet stream axis extractions closely aligned with wind field data. Full article
(This article belongs to the Special Issue Application of Machine Learning in Graphics and Images, 2nd Edition)
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20 pages, 5843 KiB  
Article
DW-MLSR: Unsupervised Deformable Medical Image Registration Based on Dual-Window Attention and Multi-Latent Space
by Yuxuan Huang, Mengxiao Yin, Zhipan Li and Feng Yang
Electronics 2024, 13(24), 4966; https://doi.org/10.3390/electronics13244966 - 17 Dec 2024
Viewed by 745
Abstract
(1) Background: In recent years, the application of Transformers and Vision Transformers (ViTs) in medical image registration has been constrained by sliding attention mechanisms, which struggle to effectively capture non-adjacent but critical structures, such as the hippocampus and ventricles in the brain. Additionally, [...] Read more.
(1) Background: In recent years, the application of Transformers and Vision Transformers (ViTs) in medical image registration has been constrained by sliding attention mechanisms, which struggle to effectively capture non-adjacent but critical structures, such as the hippocampus and ventricles in the brain. Additionally, the lack of labels in unsupervised registration often leads to overfitting. (2) To address these issues, we propose a novel method, DW-MLSR, based on dual-window attention and multi-latent space. The dual-window attention mechanism enhances the transmission of information across non-adjacent structures, while the multi-latent space improves the model’s generalization by learning latent image representations. (3) Experimental results demonstrate that DW-MLSR outperforms mainstream registration models, showcasing significant potential in medical image registration. (4) The DW-MLSR method addresses the limitations of sliding attention in transmitting information between non-adjacent windows, improves the performance of unsupervised registration, and demonstrates broad application prospects in medical image registration. Full article
(This article belongs to the Special Issue Application of Machine Learning in Graphics and Images, 2nd Edition)
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20 pages, 2467 KiB  
Article
RegMamba: An Improved Mamba for Medical Image Registration
by Xin Hu, Jiaqi Chen and Yilin Chen
Electronics 2024, 13(16), 3305; https://doi.org/10.3390/electronics13163305 - 20 Aug 2024
Cited by 5 | Viewed by 2538
Abstract
Deformable medical image registration aims to minimize the differences between fixed and moving images to provide comprehensive physiological or structural information for further medical analysis. Traditional learning-based convolutional network approaches usually suffer from the problem of perceptual limitations, and in recent years, the [...] Read more.
Deformable medical image registration aims to minimize the differences between fixed and moving images to provide comprehensive physiological or structural information for further medical analysis. Traditional learning-based convolutional network approaches usually suffer from the problem of perceptual limitations, and in recent years, the Transformer architecture has gained popularity for its superior long-range relational modeling capabilities, but still faces severe computational challenges in handling high-resolution medical images. Recently, selective state-space models have shown great potential in the vision domain due to their fast inference and efficient modeling. Inspired by this, in this paper, we propose RegMamba, a novel medical image registration architecture that combines convolutional and state-space models (SSMs), designed to efficiently capture complex correspondence in registration while maintaining efficient computational effort. Firstly our model introduces Mamba to efficiently remotely model and process potential dependencies of the data to capture large deformations. At the same time, we use a scaled convolutional layer in Mamba to alleviate the problem of spatial information loss in 3D data flattening processing in Mamba. Then, a deformable convolutional residual module (DCRM) is proposed to adaptively adjust the sampling position and process deformations to capture more flexible spatial features while learning fine-grained features of different anatomical structures to construct local correspondences and improve model perception. We demonstrate the advanced registration performance of our method on the LPBA40 and IXI public datasets. Full article
(This article belongs to the Special Issue Application of Machine Learning in Graphics and Images, 2nd Edition)
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19 pages, 17496 KiB  
Article
HR-YOLO: A Multi-Branch Network Model for Helmet Detection Combined with High-Resolution Network and YOLOv5
by Yuanfeng Lian, Jing Li, Shaohua Dong and Xingtao Li
Electronics 2024, 13(12), 2271; https://doi.org/10.3390/electronics13122271 - 10 Jun 2024
Cited by 3 | Viewed by 1536
Abstract
Automatic detection of safety helmet wearing is significant in ensuring safe production. However, the accuracy of safety helmet detection can be challenged by various factors, such as complex environments, poor lighting conditions and small-sized targets. This paper presents a novel and efficient deep [...] Read more.
Automatic detection of safety helmet wearing is significant in ensuring safe production. However, the accuracy of safety helmet detection can be challenged by various factors, such as complex environments, poor lighting conditions and small-sized targets. This paper presents a novel and efficient deep learning framework named High-Resolution You Only Look Once (HR-YOLO) for safety helmet wearing detection. The proposed framework synthesizes safety helmet wearing information from the features of helmet objects and human pose. HR-YOLO can use features from two branches to make the bounding box of suppression predictions more accurate for small targets. Then, to further improve the iterative efficiency and accuracy of the model, we design an optimized residual network structure by using Optimized Powered Stochastic Gradient Descent (OP-SGD). Moreover, a Laplace-Aware Attention Model (LAAM) is designed to make the YOLOv5 decoder pay more attention to the feature information from human pose and suppress interference from irrelevant features, which enhances network representation. Finally, non-maximum suppression voting (PA-NMS voting) is proposed to improve detection accuracy for occluded targets, using pose information to constrain the confidence of bounding boxes and select optimal bounding boxes through a modified voting process. Experimental results demonstrate that the presented safety helmet detection network outperforms other approaches and has practical value in application scenarios. Compared with the other algorithms, the proposed algorithm improves the precision, recall and mAP by 7.27%, 5.46% and 7.3%, on average, respectively. Full article
(This article belongs to the Special Issue Application of Machine Learning in Graphics and Images, 2nd Edition)
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