Image Processing and Generation, Pattern Recognition, and Data Visualization

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 371

Special Issue Editors


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Guest Editor
Faculty of Informatics, University of Debrecen, P.O Box 400, H-4002 Debrecen, Hungary
Interests: image processing; pattern recognition; machine learning

E-Mail Website
Guest Editor
Institute of Mathematics and Computer Science, Eszterhazy Karoly Catholic University, 3300 Eger, Hungary
Interests: data visualization; geometric modeling; visual understanding; visual arts
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue of the journal Mathematics entitled "Image Processing and Generation, Pattern Recognition, and Data Visualization". This Special Issue focuses on the application of mathematical principles and techniques primarily in the field of digital image processing, pattern recognition, and understanding and visualizing digital information and data, including image generation for artistic purposes. It also includes various artificial intelligence techniques such as machine learning and deep learning, which can extract significant information from complex images and help to support the understanding of visual information, as well as being used to solve either low- or high-level image processing challenges.

Artificial intelligence, machine learning, and especially deep learning have transformed the field of data visualization and understanding, digital image generation and processing, and pattern recognition by enabling the efficient interpretation of large amounts of information represented in a visual form. These new techniques have played an important role in recent years, making them a focal point of this Special Issue.

We invite researchers and practitioners to contribute papers exploring diverse aspects of artificial intelligence and machine learning in digital image generation and processing and pattern recognition, as well as data visualization and its applications.

The aim of this Special Issue is to bring together contributions that discuss problems and solutions in this area, both from a methodological and an application-oriented perspective. Topics of interest include, but are not limited to, the following:

  • Image processing;
  • Image segmentation;
  • Data visualization methods;
  • Medical image processing and analysis;
  • Machine learning and pattern recognition;
  • Pattern recognition and analysis;
  • Feature extraction and feature selection;
  • Object detection, tracking, and recognition;
  • Deep learning for image processing and pattern recognition;
  • Artificial intelligence for data visualization;
  • AI-based generation and understanding of visual artworks;
  • Performance evaluation of digital topology and its application in image processing.

Dr. Attila Fazekas
Prof. Dr. Miklos Hoffmann
Guest Editors

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Keywords

  • image processing
  • pattern recognition
  • data visualization
  • machine learning
  • artificial intelligence
  • digital topology
  • AI-based image generation

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Published Papers (1 paper)

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Research

13 pages, 3733 KiB  
Article
Comparative Model Efficiency Analysis Based on Dissimilar Algorithms for Image Learning and Correction as a Means of Fault-Finding
by Joe Benganga, Tshepo Kukuni, Ben Kotze and Lepekola Lenkoe
Mathematics 2025, 13(11), 1835; https://doi.org/10.3390/math13111835 - 30 May 2025
Viewed by 142
Abstract
The introduction of technology in different sectors to optimise efficiency is increasing rapidly. As a result of the opportunities that artificial intelligence presents to different sectors by optimally performing tasks with less error compared to humans or traditional models, the use of AI [...] Read more.
The introduction of technology in different sectors to optimise efficiency is increasing rapidly. As a result of the opportunities that artificial intelligence presents to different sectors by optimally performing tasks with less error compared to humans or traditional models, the use of AI in artefact detection is being investigated. This research paper thus presents a comparative model efficiency analysis based on dissimilar algorithms, namely CNN, VGG16, Inception_V3, and ResNet_50. The model developed was based on images that were obtained from a Toshiba CT scanner for two types of datasets (88 image datasets) and 170 image datasets, both comprising metal and ring artefacts. Furthermore, the results demonstrate higher data losses in the data transfer learning due to data recycling, suggesting that the model is prone to image feature losses when the model threshold is set at 75%. Additionally, two data transfer models were evaluated against “our model”. The results demonstrate that VGG16 performed better in terms of data accuracy than both the testing and training models, while the Resnet_50 algorithm performed poorly in terms of the loss encountered compared to the other three algorithms. Full article
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