Mathematical and Computational Methods for Image Processing and Pattern Recognition

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 819

Special Issue Editor

School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
Interests: image processing and computer graphics; intelligent data processing and analysis; deep learning

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the mathematical foundations and advanced computational methodologies in the fields of image processing and pattern recognition. With the rapid evolution of intelligent computing paradigms, researchers are increasingly exploring mathematical principles and algorithmic frameworks for novel computational models, driving breakthroughs in core areas such as feature representation theory, model architecture design, and cross-modal learning mechanisms. The Issue aims to highlight the integration of mathematical theory and computational methods, establishing a solid theoretical and methodological foundation for advances in image processing and pattern recognition.

We invite submissions presenting original research on topics including, but not limited to, the following areas:

  1. Mathematical modeling and computational methods for image processing;
  2. Theoretical foundations and algorithmic innovations in pattern recognition;
  3. Theoretical analysis and performance evaluation of intelligent computing models;
  4. Cross-modal data representation and fusion methods;
  5. Applications of optimization theory in visual computing;
  6. Balancing computational efficiency and accuracy in complex scenarios;
  7. Theoretical foundations of novel neural network architectures;
  8. Theoretical advances in unsupervised and self-supervised learning.

The Special Issue will systematically present the latest research achievements in mathematical theories and computational methods for image processing and pattern recognition, providing critical support for theoretical and technological progress in the field. We cordially invite researchers from both academia and industry to contribute their innovative work and join us in promoting the theoretical and methodological advancement of the discipline.

Dr. Ting Lan
Guest Editor

Manuscript Submission Information

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Keywords

  • image processing
  • pattern recognition
  • visual computing
  • data fusion
  • mathematical modeling
  • neural network
  • deep learning
  • machine learning

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Published Papers (1 paper)

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Research

18 pages, 3588 KB  
Article
CE-FPN-YOLO: A Contrast-Enhanced Feature Pyramid for Detecting Concealed Small Objects in X-Ray Baggage Images
by Qianxiang Cheng, Zhanchuan Cai, Yi Lin, Jiayao Li and Ting Lan
Mathematics 2025, 13(24), 4012; https://doi.org/10.3390/math13244012 - 16 Dec 2025
Viewed by 662
Abstract
Accurate detection of concealed items in X-ray baggage images is critical for public safety in high-security environments such as airports and railway stations. However, small objects with low material contrast, such as plastic lighters, remain challenging to identify due to background clutter, overlapping [...] Read more.
Accurate detection of concealed items in X-ray baggage images is critical for public safety in high-security environments such as airports and railway stations. However, small objects with low material contrast, such as plastic lighters, remain challenging to identify due to background clutter, overlapping contents, and weak edge features. In this paper, we propose a novel architecture called the Contrast-Enhanced Feature Pyramid Network (CE-FPN), designed to be integrated into the YOLO detection framework. CE-FPN introduces a contrast-guided multi-branch fusion module that enhances small-object representations by emphasizing texture boundaries and improving semantic consistency across feature levels. When incorporated into YOLO, the proposed CE-FPN significantly boosts detection accuracy on the HiXray dataset, achieving up to a +10.1% improvement in mAP@50 for the nonmetallic lighter class and an overall +1.6% gain, while maintaining low computational overhead. In addition, the model attains a mAP@50 of 84.0% under low-resolution settings and 87.1% under high-resolution settings, further demonstrating its robustness across different input qualities. These results demonstrate that CE-FPN effectively enhances YOLO’s capability in detecting small and concealed objects, making it a promising solution for real-world security inspection applications. Full article
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