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Electronics
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AI-Based Image Processing Detection and Classification Analysis for Multidisciplinary Approaches
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AI-Based Image Processing Detection and Classification Analysis for Multidisciplinary Approaches

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 3067

Special Issue Editor

Dr. Honarvar Shakibaei Asli

E-Mail Website
Guest Editor
Centre for Life-Cycle Engineering and Management, School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK
Interests: signals and systems; digital filter design; digital image processing; medical image processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores cutting-edge advancements in AI-based detection and classification analysis within the realm of image processing. Covering multidisciplinary approaches, this issue delves into the theoretical foundations and practical applications of AI technologies in various fields. From medical imaging to remote sensing and beyond, researchers and practitioners contribute their insights, methodologies, and case studies to elucidate the transformative impact of AI on image-processing techniques. This issue aims to provide a comprehensive overview of the state-of-the-art methods, challenges, and future directions in leveraging AI for enhancing image detection and classification across diverse domains.

This issue explores the theoretical underpinnings and practical applications of AI methodologies, including machine learning, deep learning, and computer vision, in addressing complex challenges in image analysis. Contributions cover a wide range of multidisciplinary domains, including medical imaging, satellite imagery, surveillance, and industrial automation. Through this collection of research articles, this Special Issue provides valuable insights into the theory and practical implementation of AI-driven solutions for image processing across diverse fields.

Artificial intelligence (AI) and image processing techniques have revolutionized the field of detection and classification analysis across various disciplines. The integration of AI algorithms with image processing methods enables the automated analysis of complex data, leading to advancements in multidisciplinary approaches. This Special Issue aims to explore the theoretical foundations and practical applications of AI-based image processing for detection and classification analysis in multidisciplinary contexts.

We welcome submissions on topics related to new theories and evolutionary methods for AI-based image processing detection and classification analysis across multidisciplinary applications. A non-exhaustive list of topics is as follows:

  • Medical image analysis;
  • Feature selection, extraction, and learning;
  • Remote sensing and earth observation;
  • Object detection and recognition;
  • Biometric recognition;
  • Image restoration and noise reduction;
  • Industrial quality control and inspection;
  • Environmental monitoring and conservation;
  • Forensic imaging and analysis;
  • Use of image transforms and moments in enhancement analysis;
  • Art and cultural heritage preservation;
  • Human activity recognition and behaviour analysis;
  • Ethical and societal implications of AI in image processing.

Dr. Honarvar Shakibaei Asli
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Electronics 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 2400 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 detection
  • image analysis
  • artificial intelligence

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Published Papers (2 papers)

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19 pages, 2703 KiB  
Article
DualNetIQ: Texture-Insensitive Image Quality Assessment with Dual Multi-Scale Feature Maps
by Adel Agamy, Hossam Mady, Hamada Esmaiel, Abdulrahman Al Ayidh, Abdelmageed Mohamed Aly and Mohamed Abdel-Nasser
Electronics 2025, 14(6), 1169; https://doi.org/10.3390/electronics14061169 - 17 Mar 2025
Viewed by 341
Abstract
The precise assessment of image quality that matches human perception is still a major challenge in the field of digital imaging. Digital images play a crucial role in many technological and media applications. The existing deep convolutional neural network (CNN)-based image quality assessment [...] Read more.
The precise assessment of image quality that matches human perception is still a major challenge in the field of digital imaging. Digital images play a crucial role in many technological and media applications. The existing deep convolutional neural network (CNN)-based image quality assessment (IQA) methods have advanced considerably, but there remains a critical need to improve the performance of existing methods while maintaining explicit tolerance to visual texture resampling and texture similarity. This paper introduces DualNetIQ, a novel full-reference IQA method that leverages the strengths of deep learning architectures to exhibit robustness against resampling effects on visual textures. DualNetIQ includes two main stages: feature extraction from the reference and distorted images, and similarity measurement based on combining global texture and structure similarity metrics. In particular, DualNetIQ takes features from input images using a group of hybrid pre-trained multi-scale feature maps carefully chosen from VGG19 and SqueezeNet pre-trained CNN models to find differences in texture and structure between the reference image and the distorted image. The Grey Wolf Optimizer (GWO) calculates the weighted combination of global texture and structure similarity metrics to assess the similarity between reference and distorted images. The unique advantage of the proposed method is that it does not require training or fine-tuning the CNN deep learning model. Comprehensive experiments and comparisons on five databases, including various distortion types, demonstrate the superiority of the proposed method over state-of-the-art models, particularly in image quality prediction and texture similarity tasks. Full article
(This article belongs to the Special Issue AI-Based Image Processing Detection and Classification Analysis for Multidisciplinary Approaches)
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43 pages, 5618 KiB  
Article
Motion Prediction and Object Detection for Image-Based Visual Servoing Systems Using Deep Learning
by Zhongwen Hao, Deli Zhang and Barmak Honarvar Shakibaei Asli
Electronics 2024, 13(17), 3487; https://doi.org/10.3390/electronics13173487 - 2 Sep 2024
Viewed by 2482
Abstract
This study primarily investigates advanced object detection and time series prediction methods in image-based visual servoing systems, aiming to capture targets better and predict the motion trajectory of robotic arms in advance, thereby enhancing the system’s performance and reliability. The research first implements [...] Read more.
This study primarily investigates advanced object detection and time series prediction methods in image-based visual servoing systems, aiming to capture targets better and predict the motion trajectory of robotic arms in advance, thereby enhancing the system’s performance and reliability. The research first implements object detection on the VOC2007 dataset using the Detection Transformer (DETR) and achieves ideal detection scores. The particle swarm optimization algorithm and 3-5-3 polynomial interpolation methods were utilized for trajectory planning, creating a unique dataset through simulation. This dataset contains randomly generated trajectories within the workspace, fully simulating actual working conditions. Significantly, the Bidirectional Long Short-Term Memory (BILSTM) model was improved by substituting its traditional Multilayer Perceptron (MLP) components with Kolmogorov–Arnold Networks (KANs). KANs, inspired by the K-A theorem, improve the network representation ability by placing learnable activation functions on fixed node activation functions. By implementing KANs, the model enhances parameter efficiency and interpretability, thus addressing the typical challenges of MLPs, such as the high parameter count and lack of transparency. The experiments achieved favorable predictive results, indicating that the KAN not only reduces the complexity of the model but also improves learning efficiency and prediction accuracy in dynamic visual servoing environments. Finally, Gazebo software was used in ROS to model and simulate the robotic arm, verify the effectiveness of the algorithm, and achieve visual servoing. Full article
(This article belongs to the Special Issue AI-Based Image Processing Detection and Classification Analysis for Multidisciplinary Approaches)
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