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Mathematics
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Mathematics Methods in Image Processing and Computer Vision
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Mathematics Methods in Image Processing and Computer Vision

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 2798

Special Issue Editor

Dr. Yuwu Lu

E-Mail Website
Guest Editor
School of Software, South China Normal University, Guangzhou, China
Interests: image classification; face recognition

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, titled “Mathematics Methods in Image Processing and Computer Vision”, featuring original research articles focused on theoretical or data-driven contributions for solving real problems in challenging research areas, such as image processing or computer vision. This Special Issue is dedicated to exploring the realm of numerical modeling in the complex domain of image representation and computer vision. The topics to be covered include, but are not confined to, pattern recognition and machine learning, image processing and computer vision, biometric recognition, feature extraction and feature selection, cross-modal learning, optimization learning methods, medical image analysis, and document analysis and recognition.

The advancement of computational technology has paved the way for the integration of complex numerical models and high-performance simulations in various engineering fields. As an important field of artificial intelligence, image representation and computer vision are often considered as the key bridges for human–computer interaction, as they combine expertise from these two fields to solve problems related to machine vision. The establishment of mathematical models provides more effective theoretical guidance for complex image representation and machine vision problems and plays an important role in solving complex problems.

Dr. Yuwu Lu
Guest Editor

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

  • pattern recognition and machine learning
  • image processing and computer vision
  • biometric recognition
  • feature extraction and feature selection
  • cross-modal learning
  • optimization and learning methods
  • medical image analysis
  • document analysis and recognition

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

Published Papers (4 papers)

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Research

17 pages, 2685 KiB  
Article
DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation
by Yongchao Duan, Rui Yang, Ming Zhao, Mingrui Qi and Sheng-Lung Peng
Mathematics 2025, 13(9), 1454; https://doi.org/10.3390/math13091454 - 29 Apr 2025
Abstract
Segmentation of retinal vessels from fundus images is critical for diagnosing diseases such as diabetes and hypertension. However, the inherent challenges posed by the complex geometries of vessels and the highly imbalanced distribution of thick versus thin vessel pixels demand innovative solutions for [...] Read more.
Segmentation of retinal vessels from fundus images is critical for diagnosing diseases such as diabetes and hypertension. However, the inherent challenges posed by the complex geometries of vessels and the highly imbalanced distribution of thick versus thin vessel pixels demand innovative solutions for robust feature extraction. In this paper, we introduce DAF-UNet, a novel architecture that integrates advanced modules to address these challenges. Specifically, our method leverages a pre-trained deformable convolution (DC) module within the encoder to dynamically adjust the sampling positions of the convolution kernel, thereby adapting the receptive field to capture irregular vessel morphologies more effectively than traditional convolutional approaches. At the network’s bottleneck, an enhanced atrous spatial pyramid pooling (ASPP) module is employed to extract and fuse rich, multi-scale contextual information, significantly improving the model’s capacity to delineate vessels of varying calibers. Furthermore, we propose a hybrid loss function that combines pixel-level and segment-level losses to robustly address the segmentation inconsistencies caused by the disparity in vessel thickness. Experimental evaluations on the DRIVE and CHASE_DB1 datasets demonstrated that DAF-UNet achieved a global accuracy of 0.9572/0.9632 and a Dice score of 0.8298/0.8227, respectively, outperforming state-of-the-art methods. These results underscore the efficacy of our approach in precisely capturing fine vascular details and complex boundaries, marking a significant advancement in retinal vessel segmentation. Full article
(This article belongs to the Special Issue Mathematics Methods in Image Processing and Computer Vision)
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21 pages, 11655 KiB  
Article
A Novel Deep Learning Zero-Watermark Method for Interior Design Protection Based on Image Fusion
by Yiran Peng, Qingqing Hu, Jing Xu, KinTak U and Junming Chen
Mathematics 2025, 13(6), 947; https://doi.org/10.3390/math13060947 - 13 Mar 2025
Viewed by 456
Abstract
Interior design, which integrates art and science, is vulnerable to infringements such as copying and tampering. The unique and often intricate nature of these designs makes them vulnerable to unauthorized replication and misuse, posing significant challenges for designers seeking to protect their intellectual [...] Read more.
Interior design, which integrates art and science, is vulnerable to infringements such as copying and tampering. The unique and often intricate nature of these designs makes them vulnerable to unauthorized replication and misuse, posing significant challenges for designers seeking to protect their intellectual property. To solve the above problems, we propose a deep learning-based zero-watermark copyright protection method. The method aims to embed undetectable and unique copyright information through image fusion technology without destroying the interior design image. Specifically, the method fuses the interior design and a watermark image through deep learning to generate a highly robust zero-watermark image. This study also proposes a zero-watermark verification network with U-Net to verify the validity of the watermark and extract the copyright information efficiently. This network can accurately restore watermark information from protected interior design images, thus effectively proving the copyright ownership of the work and the copyright ownership of the interior design. According to verification on an experimental dataset, the zero-watermark copyright protection method proposed in this study is robust against various image-oriented attacks. It avoids the problem of image quality loss that traditional watermarking techniques may cause. Therefore, this method can provide a strong means of copyright protection in the field of interior design. Full article
(This article belongs to the Special Issue Mathematics Methods in Image Processing and Computer Vision)
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17 pages, 1237 KiB  
Article
TraceGuard: Fine-Tuning Pre-Trained Model by Using Stego Images to Trace Its User
by Limengnan Zhou, Xingdong Ren, Cheng Qian and Guangling Sun
Mathematics 2024, 12(21), 3333; https://doi.org/10.3390/math12213333 - 24 Oct 2024
Cited by 1 | Viewed by 1023
Abstract
Currently, a significant number of pre-trained models are published online to provide services to users owing to the rapid maturation and popularization of machine learning as a service (MLaaS). Some malicious users have pre-trained models illegally to redeploy them and earn money. However, [...] Read more.
Currently, a significant number of pre-trained models are published online to provide services to users owing to the rapid maturation and popularization of machine learning as a service (MLaaS). Some malicious users have pre-trained models illegally to redeploy them and earn money. However, most of the current methods focus on verifying the copyright of the model rather than tracing responsibility for the suspect model. In this study, TraceGuard is proposed, the first framework based on steganography for tracing a suspect self-supervised learning (SSL) pre-trained model, to ascertain which authorized user illegally released the suspect model or if the suspect model is independent. Concretely, the framework contains an encoder and decoder pair and the SSL pre-trained model. Initially, the base pre-trained model is frozen, and the encoder and decoder are jointly learned to ensure the two modules can embed the secret key into the cover image and extract the secret key from the embedding output by the base pre-trained model. Subsequently, the base pre-trained model is fine-tuned using stego images to implement a fingerprint while the encoder and decoder are frozen. To assure the effectiveness and robustness of the fingerprint and the utility of fingerprinted pre-trained models, three alternate steps of model stealing simulations, fine-tuning for uniqueness, and fine-tuning for utility are designed. Finally, the suspect pre-trained model is traced to its user by querying stego images. Experimental results demonstrate that TraceGuard can reliably trace suspect models and is robust against common fingerprint removal attacks such as fine-tuning, pruning, and model stealing. In the future, we will further improve the robustness against model stealing attack. Full article
(This article belongs to the Special Issue Mathematics Methods in Image Processing and Computer Vision)
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17 pages, 3576 KiB  
Article
Towards Discriminability with Distribution Discrepancy Constrains for Multisource Domain Adaptation
by Yuwu Lu and Wanming Huang
Mathematics 2024, 12(16), 2564; https://doi.org/10.3390/math12162564 - 20 Aug 2024
Cited by 2 | Viewed by 803
Abstract
Multisource domain adaptation (MDA) is committed to mining and extracting data concerning target tasks from several source domains. Many recent studies have focused on extracting domain-invariant features to eliminate domain distribution differences. However, there are three aspects that require further consideration. (1) Efforts [...] Read more.
Multisource domain adaptation (MDA) is committed to mining and extracting data concerning target tasks from several source domains. Many recent studies have focused on extracting domain-invariant features to eliminate domain distribution differences. However, there are three aspects that require further consideration. (1) Efforts should be made to ensure the maximum correlation in the potential subspace between the source and target domains. (2) While aligning the marginal distribution, the conditional distribution must also be considered. (3) Merely aligning the source distribution and target distribution cannot guarantee sufficient differentiation for classification tasks. To address these problems, we propose a novel approach named towards discriminability with distribution discrepancy constrains for multisource domain adaptation (TD-DDC). Specifically, TD-DDC first mines features of maximal relations learned from all domains while constructing domain data distribution mean distance metrics for interdomain distribution adaptation. Simultaneously, we integrate discriminability into domain alignment, which means increasing the distance among labels that are distinct from one another while reducing the distance among labels that are the same. Our proposed method not only reduces the interdomain distributional differences but also takes into account the preservation of interdomain correlation and inter-category discrimination. Numerous experiments have shown that TD-DDC performs much better than its competitors on three visual benchmark test databases. Full article
(This article belongs to the Special Issue Mathematics Methods in Image Processing and Computer Vision)
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