Intelligent Computing with Applications in Computer Vision

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 1364

Special Issue Editors

School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: robot vision; intelligent detection; deep learning
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Guest Editor
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: robotics control; embedded system; sensor technology
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: robotics control; multi-agent system; distributed optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few years, there has been remarkable growth in the effectiveness and influence of computer vision technologies across a multitude of disciplines including biomedical imaging, industrial automation, and healthcare informatics, among others.  However, there are still many significant problems encountered in the research and application of computer vision technology and artificial intelligence. In light of these developments, in this Special Issue, we will compile state-of-the-art research and applied innovations within the realm of computational intelligence, with a particular emphasis on its intersections with image processing and computer vision.  

This collection will delve into a variety of themes, extending beyond the basic to encompass applications of image processing and computer vision methodologies. Areas of interest include (but are not restricted to) the following:

  • Image processing
  • Neural networks
  • Deep learning for efficient detection and segmentation
  • Deep learning for medical image analysis
  • Industrial applications of AI
  • Expert systems integrated with image processing and computer vision
  • Multi-modal image analysis
  • Deep learning theory and applications
  • Supervised/semi-supervised/unsupervised learning
  • Emerging trends in explainable AI (XAI)

We also invite authors to submit extended versions of their conference papers to this Special Issue. All papers accepted in this Special Issue will meet our usual high standards for publication in Mathematics.

Dr. Lei Yang
Prof. Dr. En Li
Dr. Fangyuan Li
Guest Editors

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. Mathematics 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 2600 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
  • computer vision
  • neural networks
  • machine learning
  • deep learning
  • artificial intelligence
  • computational intelligence

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Published Papers (1 paper)

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Research

17 pages, 21415 KiB  
Article
A Novel Method for Localized Typical Blemish Image Data Generation in Substations
by Na Zhang, Jingjing Fan, Gang Yang, Guodong Li, Hong Yang and Yang Bai
Mathematics 2024, 12(18), 2950; https://doi.org/10.3390/math12182950 - 23 Sep 2024
Viewed by 870
Abstract
Current mainstream methods for detecting surface blemishes on substation equipment typically rely on extensive sets of blemish images for training. However, the unpredictable nature and infrequent occurrence of such blemishes present significant challenges in data collection. To tackle these issues, this paper proposes [...] Read more.
Current mainstream methods for detecting surface blemishes on substation equipment typically rely on extensive sets of blemish images for training. However, the unpredictable nature and infrequent occurrence of such blemishes present significant challenges in data collection. To tackle these issues, this paper proposes a novel approach for generating localized, representative blemish images within substations. Firstly, to mitigate global style variations in images generated by generative adversarial networks (GANs), we developed a YOLO-LRD method focusing on local region detection within equipment. This method enables precise identification of blemish locations in substation equipment images. Secondly, we introduce a SEB-GAN model tailored specifically for generating blemish images within substations. By confining blemish generation to identified regions within equipment images, the authenticity and diversity of the generated defect data are significantly enhanced. Theexperimental results validate that the YOLO-LRD and SEB-GAN techniques effectively create precise datasets depicting flaws in substations. Full article
(This article belongs to the Special Issue Intelligent Computing with Applications in Computer Vision)
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