Computer Vision, Image Processing Technologies and Machine Learning

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

Deadline for manuscript submissions: 10 December 2025 | Viewed by 865

Special Issue Editors


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Guest Editor
Department of Information Technology Management, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
Interests: AI; computer vision; image processing; integration of AI with IoT

E-Mail Website
Guest Editor
Applied Computer Science Programme, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand
Interests: computer vision; image processing; machine learning; human-computer interaction

Special Issue Information

Dear Colleagues,

The fields of computer vision, image processing, and machine learning have witnessed remarkable advancements in recent years, driving significant innovations across industries. The integration of these technologies has created transformative solutions for real-time image analysis, automation, healthcare, robotics, security, entertainment, and beyond. This Special Issue aims to highlight the cutting-edge research, methodologies, and applications that leverage the synergy between computer vision, image processing, and machine learning.

Computer vision enables machines to interpret and understand the visual world, closely mimicking human perception. From object detection and recognition to scene understanding and autonomous navigation, computer vision plays a central role in enabling intelligent systems to function in complex, dynamic environments. Researchers are exploring new ways to enhance computer vision techniques with deep learning algorithms, allowing for more accurate and scalable solutions.

Image processing is the backbone of many computer vision applications, focusing on improving and analyzing visual data. This area includes various techniques like noise reduction, image enhancement, image segmentation, and edge detection, which refine raw images or videos for further analysis. Innovations in image processing have facilitated medical imaging advancements, satellite image analysis, and high-quality video processing in entertainment and surveillance.

Machine learning forms the core of modern artificial intelligence and is used to empower computer vision and image processing systems to learn from large datasets, adapt to new information, and improve over time. In particular, deep learning, a subset of machine learning, has revolutionized both computer vision and image processing by enabling high-performance models for image classification, object detection, face recognition, and scene reconstruction.

This Special Issue brings together multidisciplinary approaches, covering topics such as the following:

  • Novel machine learning techniques for computer vision;
  • Image processing for medical imaging and diagnostics;
  • Real-time object detection and tracking;
  • Facial recognition and biometrics;
  • Deep learning for image and video enhancement;
  • Applications of computer vision and machine learning in autonomous vehicles;
  • Innovative algorithms for noise removal and edge detection;
  • Smart surveillance and security systems.

Researchers, engineers, and practitioners are invited to contribute original research, reviews, and case studies that explore innovative applications and new challenges in the field of computer vision, image processing, and machine learning. The goal is to provide a comprehensive overview of current trends and future directions for these interconnected fields, pushing the boundaries of what is possible in intelligent visual systems.

Dr. Mahasak Ketcham
Dr. Thittaporn Ganokratanaa
Guest Editors

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Keywords

  • computer vision
  • image processing
  • machine learning
  • deep learning
  • object detection
  • image enhancement
  • facial recognition
  • medical imaging
  • autonomous vehicles
  • real-time analysis
  • smart surveillance
  • video processing

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Published Papers (1 paper)

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Research

22 pages, 1970 KiB  
Article
Enhanced Intrusion Detection Using Conditional-Tabular-Generative-Adversarial-Network-Augmented Data and a Convolutional Neural Network: A Robust Approach to Addressing Imbalanced Cybersecurity Datasets
by Shridhar Allagi, Toralkar Pawan and Wai Yie Leong
Mathematics 2025, 13(12), 1923; https://doi.org/10.3390/math13121923 - 10 Jun 2025
Abstract
Intrusion prevention and classification are common in the research field of cyber security. Models built from training data may fail to prevent or classify intrusions accurately if the dataset is imbalanced. Most researchers employ SMOTE to balance the dataset. SMOTE in turn fails [...] Read more.
Intrusion prevention and classification are common in the research field of cyber security. Models built from training data may fail to prevent or classify intrusions accurately if the dataset is imbalanced. Most researchers employ SMOTE to balance the dataset. SMOTE in turn fails to address the constraints associated with the dataset, such as diverse data types, preserving the data distribution, capturing non-linear relationships, and preserving oversampling noise. The novelty of this work is in addressing the issues associated with data distribution and SMOTE by employing Conditional Tabular Generative Adversarial Networks (CTGANs) on NSL_KDD and UNSW_NB15 datasets. The balanced input corpus is fed into the CNN model to predict the intrusion. The CNN model involves two convolution layers, max-pooling, ReLU as the activation layer, and a dense layer. The proposed work employs measures such as accuracy, recall, precision, specificity and F1-score for measuring the model performance. The study shows that CTGAN improves the intrusion detection rate. This research highlights the high-quality synthetic samples generated by CTGAN that significantly enhance CNN-based intrusion detection performance on imbalance datasets. This demonstrates the potential for deploying GAN-based oversampling techniques in real-world cybersecurity systems to improve detection accuracy and reduce false negatives. Full article
(This article belongs to the Special Issue Computer Vision, Image Processing Technologies and Machine Learning)
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