Deep Learning and Machine Learning for Image Processing: Algorithms and Applications

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 28 February 2025 | Viewed by 766

Special Issue Editor


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Guest Editor
School of Information Science and Technology, Donghua University, Shanghai 201620, China
Interests: image analysis theory and methods (remote sensing, medicine, fiber, natural images, etc.); machine learning and pattern recognition (deep learning, tensor analysis, classification, segmentation, regression, etc.); intelligent system development and applications

Special Issue Information

Dear Colleagues,

Deep learning and machine learning have revolutionized the field of image processing, offering unprecedented levels of accuracy and efficiency in tasks such as object detection, image classification, image generation, and image segmentation. This Special Issue aims to bring together state-of-the-art research on deep learning and machine learning techniques for image processing, with a focus on algorithms and applications.

Topics of interest include, but are not limited to, the following:

  • Deep learning models for image classification and object detection;
  • Generative adversarial networks for image synthesis and editing;
  • Machine learning approaches for image segmentation and localization;
  • Transfer learning and domain adaptation for image processing;
  • Fusion of deep learning and conventional techniques for image enhancement;
  • Applications of deep learning and machine learning in medical imaging, remote sensing, computer vision, and more.

Authors are invited to submit original research articles, reviews, and short communications addressing the above topics. Extended conference papers are also welcome, but they should contain at least 50% of new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases.

Dr. Zhao Chen
Guest Editor

Manuscript Submission Information

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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. Computers is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • deep learning
  • machine learning
  • image processing
  • computer vision
  • feature extraction
  • image classification
  • object detection
  • image segmentation
  • generative adversarial networks

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Published Papers (1 paper)

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Research

24 pages, 2096 KiB  
Article
Human Activity Recognition Using Graph Structures and Deep Neural Networks
by Abed Al Raoof K. Bsoul
Computers 2025, 14(1), 9; https://doi.org/10.3390/computers14010009 - 30 Dec 2024
Viewed by 501
Abstract
Human activity recognition (HAR) systems are essential in healthcare, surveillance, and sports analytics, enabling automated movement analysis. This research presents a novel HAR system combining graph structures with deep neural networks to capture both spatial and temporal patterns in activities. While CNN-based models [...] Read more.
Human activity recognition (HAR) systems are essential in healthcare, surveillance, and sports analytics, enabling automated movement analysis. This research presents a novel HAR system combining graph structures with deep neural networks to capture both spatial and temporal patterns in activities. While CNN-based models excel at spatial feature extraction, they struggle with temporal dynamics, limiting their ability to classify complex actions. To address this, we applied the Firefly Optimization Algorithm to fine-tune the hyperparameters of both the graph-based model and a CNN baseline for comparison. The optimized graph-based system, evaluated on the UCF101 and Kinetics-400 datasets, achieved 88.9% accuracy with balanced precision, recall, and F1-scores, outperforming the baseline. It demonstrated robustness across diverse activities, including sports, household routines, and musical performances. This study highlights the potential of graph-based HAR systems for real-world applications, with future work focused on multi-modal data integration and improved handling of occlusions to enhance adaptability and performance. Full article
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