Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.4
2.3
Journals
Mathematics
Special Issues
Advanced Mathematical Methods in Image Processing and Feature Recognition
share announcement

Advanced Mathematical Methods in Image Processing and Feature Recognition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 676

Editor

Dr. Guodong Wang

E-Mail Website
Guest Editor
College of Computer Science and Technology, Qingdao University, 308 Ningxia Road, Qingdao 266071, China
Interests: artificial intelligence; variational image science; deep learning; facial recognition; intelligent video surveillance and analysis; 3D reconstruction

Special Issue Information

Dear Colleagues,

This Special Issue focuses on cutting-edge mathematical theories, modeling approaches and numerical algorithms in the field of image processing and feature recognition. It highlights innovative applications of mathematical tools, including variational principles, optimization theory, matrix analysis, geometric computing, statistical learning and deep learning, to image restoration, segmentation, detection, feature extraction, object recognition, 3D reconstruction and intelligent visual analysis. The Special Issue aims to build an academic platform for interdisciplinary research between mathematics and computer vision, and promote the in-depth integration and practical deployment of mathematical methods in engineering problems surrounding image processing and pattern recognition.

Dr. Guodong Wang
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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
  • feature recognition
  • variational methods
  • deep learning
  • mathematical modeling

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 2053 KB  
Article
Research on Waste Image Classification Algorithm Based on Improved YOLOv8
by Jiaxuan Song, Tingshan Chen, Hanyun Fang, Zhenyu Liu, Yue Yu and Rui Zhang
Mathematics 2026, 14(14), 2511; https://doi.org/10.3390/math14142511 - 12 Jul 2026
Viewed by 380
Abstract
With the acceleration of urbanization, the production of household waste continues to increase, while traditional manual sorting methods suffer from low efficiency, high cost, and unstable accuracy. Deep-learning-based image classification technology provides an effective solution for automated waste classification. However, household waste images [...] Read more.
With the acceleration of urbanization, the production of household waste continues to increase, while traditional manual sorting methods suffer from low efficiency, high cost, and unstable accuracy. Deep-learning-based image classification technology provides an effective solution for automated waste classification. However, household waste images present challenges such as large variations in object scale, complex backgrounds, densely packed small objects, and easily confusable categories, making it difficult for existing models to meet practical classification requirements. To address these issues, this paper proposes BE-YOLOv8, an improved household waste image classification model based on YOLOv8, which integrates multiple strategies including data augmentation, attention mechanisms, and edge feature enhancement. First, to tackle the problems of limited training samples and class imbalance, an improved LMix data augmentation method is proposed. By introducing a label smoothing strategy, dynamically correcting mixed label weights, and adding a regularization penalty term to the loss function, the generalization ability of the model is effectively improved. Second, an Edge-Guided Multi-Scale Hybrid (EGMSH) attention mechanism is designed, which enhances the model′s perception of edge contours and multi-scale texture features through online edge computation, multi-scale depthwise separable convolutions, and adaptive gating fusion. Finally, a learnable BoundaryEdge feature enhancement module is proposed, which utilizes a trainable color projection layer and fixed-weight Sobel operators to generate high-quality edge features online and embeds them into the network via residual connections, significantly improving the discrimination of shape-similar and easily confusable categories. Experiments are conducted on a household waste image dataset containing 26,994 images across 20 categories. The results demonstrate that BE-YOLOv8 achieves a Top-1 accuracy of 83.5% on the test set, improving by 1.1% over the baseline YOLOv8. The hazardous waste category exhibits the most significant improvement, with a Top-1 accuracy of 92.5%. It is demonstrated that the model has excellent robustness in scenarios with complex backgrounds and easily confusable categories, providing a high-precision technical solution for practical waste classification applications. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Image Processing and Feature Recognition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop