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Mathematics
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Advanced Research in Neural Networks, Machine Learning, and Image Processing
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Advanced Research in Neural Networks, Machine Learning, and Image Processing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 3201

Special Issue Editors

Dr. Chengwei Pan

E-Mail Website
Guest Editor
Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: virtual/augmented reality; computer graphics; computer vision; medical image processing
Prof. Dr. Hongyi Li

E-Mail Website
Guest Editor
Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: big data; mathematical characterization methods and theoretical research of electromagnetic interference signals
Prof. Dr. Di Zhao

E-Mail Website
Guest Editor
School of Mathematical Sciences, Beihang University, Beijing 100191, China
Interests: Information security; matrix theory application and computation; signal processing; artificial intelligence; software development

Special Issue Information

Dear Colleagues,

Image processing involves manipulating visual data to enhance image quality and extract useful information, serving as an essential preprocessing step in many fields, such as computer vision, medical imaging, and remote sensing. Over the past decade, the rapid development of neural networks and machine learning has significantly advanced the field of image processing. This Special Issue, titled "Advanced Research in Neural Networks, Machine Learning, and Image Processing", aims to highlight recent advances in these areas and focuses on the application of mathematical theories and methods in the intersection of artificial intelligence and image processing. Developing mathematical models offers more effective theoretical guidance for complex image processing and machine vision issues, playing a crucial role in addressing complex problems.

We encourage researchers to contribute original, high-quality papers that explore new theories, models, and applications. Topics of interest include, but are not limited to, pattern recognition and machine learning, deep learning and neural networks, image processing and computer vision, image generation and synthesis, medical image processing and analysis, visual large language models, and various applications of image processing.

Dr. Chengwei Pan
Prof. Dr. Hongyi Li
Prof. Dr. Di Zhao
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. Mathematics 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 2600 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
  • pattern recognition
  • computer vision
  • deep learning
  • machine learning
  • neural networks
  • medical image analysis
  • large language model
  • image generation and synthesis
  • complex signals
  • signal modulation
  • signal detection
  • inverse problems
  • sparse representation
  • sampling methods
  • phase retrieval
  • knowledge graph

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

Published Papers (3 papers)

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20 pages, 33320 KiB  
Article
Two-Stage Video Violence Detection Framework Using GMFlow and CBAM-Enhanced ResNet3D
by Mohamed Mahmoud, Bilel Yagoub, Mostafa Farouk Senussi, Mahmoud Abdalla, Mahmoud Salaheldin Kasem and Hyun-Soo Kang
Mathematics 2025, 13(8), 1226; https://doi.org/10.3390/math13081226 - 8 Apr 2025
Viewed by 383
Abstract
Video violence detection has gained significant attention in recent years due to its applications in surveillance and security. This paper proposes a two-stage framework for detecting violent actions in video sequences. The first stage leverages GMFlow, a pre-trained optical flow network, to capture [...] Read more.
Video violence detection has gained significant attention in recent years due to its applications in surveillance and security. This paper proposes a two-stage framework for detecting violent actions in video sequences. The first stage leverages GMFlow, a pre-trained optical flow network, to capture the temporal motion between consecutive frames, effectively encoding motion dynamics. In the second stage, we integrate these optical flow images with RGB frames and feed them into a CBAM-enhanced ResNet3D network to capture complementary spatiotemporal features. The attention mechanism provided by CBAM enables the network to focus on the most relevant regions in the frames, improving the detection of violent actions. We evaluate the proposed framework on three widely used datasets: Hockey Fight, Crowd Violence, and UBI-Fight. Our experimental results demonstrate superior performance compared to several state-of-the-art methods, achieving AUC scores of 0.963 on UBI-Fight and accuracies of 97.5% and 94.0% on Hockey Fight and Crowd Violence, respectively. The proposed approach effectively combines GMFlow-generated optical flow with deep 3D convolutional networks, providing robust and efficient detection of violence in videos. Full article
(This article belongs to the Special Issue Advanced Research in Neural Networks, Machine Learning, and Image Processing)
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15 pages, 6773 KiB  
Article
Golden Angle Modulation in Complex Dimension Two
by Kejia Hu, Hongyi Li, Di Zhao and Yuan Jiang
Mathematics 2025, 13(3), 414; https://doi.org/10.3390/math13030414 - 26 Jan 2025
Viewed by 603
Abstract
In this paper, we propose a new geometric-shaping design for golden angle modulation (GAM) based on the complex geometric properties of open symmetrized bidisc, termed Bd-GAM, for future generation wireless communication systems. Inspired from the circular symmetric structure of the GAM, we construct [...] Read more.
In this paper, we propose a new geometric-shaping design for golden angle modulation (GAM) based on the complex geometric properties of open symmetrized bidisc, termed Bd-GAM, for future generation wireless communication systems. Inspired from the circular symmetric structure of the GAM, we construct the modulation schemes, Bd-GAM1 and Bd-GAM2. Specifically, we consider MI-optimized probabilistic modulation scheme with the geometrics properties of symmetric bidisc. With minimum SNR and entropy constraint, Bd-GAM1 and Bd-GAM2 can overcome the shaping-loss. Compared with the existed golden angle modulation introduced, the new design improves the mutual information, and the distance between adjacent constellation points. Full article
(This article belongs to the Special Issue Advanced Research in Neural Networks, Machine Learning, and Image Processing)
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40 pages, 49128 KiB  
Article
Self-Supervised Autoencoders for Visual Anomaly Detection
by Alexander Bauer, Shinichi Nakajima and Klaus-Robert Müller
Mathematics 2024, 12(24), 3988; https://doi.org/10.3390/math12243988 - 18 Dec 2024
Cited by 2 | Viewed by 1559
Abstract
We focus on detecting anomalies in images where the data distribution is supported by a lower-dimensional embedded manifold. Approaches based on autoencoders have aimed to control their capacity either by reducing the size of the bottleneck layer or by imposing sparsity constraints on [...] Read more.
We focus on detecting anomalies in images where the data distribution is supported by a lower-dimensional embedded manifold. Approaches based on autoencoders have aimed to control their capacity either by reducing the size of the bottleneck layer or by imposing sparsity constraints on their activations. However, none of these techniques explicitly penalize the reconstruction of anomalous regions, often resulting in poor detection. We tackle this problem by adapting a self-supervised learning regime that essentially implements a denoising autoencoder with structured non-i.i.d. noise. Informally, our objective is to regularize the model to produce locally consistent reconstructions while replacing irregularities by acting as a filter that removes anomalous patterns. Formally, we show that the resulting model resembles a nonlinear orthogonal projection of partially corrupted images onto the submanifold of uncorrupted examples. Furthermore, we identify the orthogonal projection as an optimal solution for a specific regularized autoencoder related to contractive and denoising variants. In addition, orthogonal projection provides a conservation effect by largely preserving the original content of its arguments. Together, these properties facilitate an accurate detection and localization of anomalous regions by means of the reconstruction error. We support our theoretical analysis by achieving state-of-the-art results (image/pixel-level AUROC of 99.8/99.2%) on the MVTec AD dataset—a challenging benchmark for anomaly detection in the manufacturing domain. Full article
(This article belongs to the Special Issue Advanced Research in Neural Networks, Machine Learning, and Image Processing)
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