AI-Based Image Processing and Computer Vision

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 20 March 2025 | Viewed by 228

Special Issue Editor


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Guest Editor
Science and Engineering Faculty, Saga University, Saga City 840-8502, Japan
Interests: artificial intelligence; big data analysis; computer vision; human-computer interaction; modeling and simulation; satellite remote sensing; image processing and analysis

Special Issue Information

Dear Colleagues,

In recent years, AI-based image processing and computer vision have made remarkable progress, and they are being put into practical use across various fields. Furthermore, technological advances in deep learning have significantly improved the accuracy of image recognition. It is now possible to perform tasks with high precision, such as human face recognition and object detection, which were difficult with conventional image recognition technology. Technological advances in generative models (such as GAN) have also made it possible to generate high-quality images that look as if they had been created by humans. This technology is used in fields such as image editing and advertising production. Moreover, image restoration technology removes noise from images and complements missing parts, and such technology that utilizes AI has been developed, thereby making it possible to restore more natural and high-quality images. Moreover, 3D recognition technology using 3D sensors and deep learning has progressed rapidly, and it is utilized in fields such as robotics and autonomous driving. Video analysis technology recognizes the movement of people and objects from videos, analyzes their actions, and is used in surveillance cameras and security systems. Meanwhile, augmented reality (AR) and virtual reality (VR), when combined with AI technology, can provide a more realistic and immersive experience. This technology is used in fields such as entertainment and education. In addition, AI technology is expected to generate new innovations by merging with other technologies such as robotics, autonomous driving, and medical care. However, ethical issues have also arisen with the development of AI. For example, if AI is misused, problems such as privacy invasion and discrimination may occur. It is important to respond appropriately to these issues. Accordingly, the following research areas are selected for this Special Issue: AI-based image processing and computer vision, pattern analysis, machine intelligence, pattern recognition, and image understanding. Your contributions would be highly appreciated.

Prof. Dr. Kohei Arai
Guest Editor

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Keywords

  • image processing
  • computer vision
  • pattern analysis
  • machine intelligence
  • pattern recognition
  • image understanding

Published Papers (1 paper)

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Research

12 pages, 5319 KiB  
Article
A Method for Maintaining a Unique Kurume Kasuri Pattern of Woven Textile Classified by EfficientNet by Means of LightGBM-Based Prediction of Misalignments
by Kohei Arai, Jin Shimazoe and Mariko Oda
Information 2024, 15(8), 434; https://doi.org/10.3390/info15080434 - 26 Jul 2024
Abstract
Methods for evaluating the fluctuation of texture patterns that are essentially regular have been proposed in the past, but the best method has not been determined. Here, as an attempt at this, we propose a method that applies AI technology (learning EfficientNet, which [...] Read more.
Methods for evaluating the fluctuation of texture patterns that are essentially regular have been proposed in the past, but the best method has not been determined. Here, as an attempt at this, we propose a method that applies AI technology (learning EfficientNet, which is widely used as a classification problem solving method) to determine when the fluctuation exceeds the tolerable limit and what the acceptable range is. We also apply this to clarify the tolerable limit of fluctuation in the “Kurume Kasuri” pattern, which is unique to the Chikugo region of Japan, and devise a method to evaluate the fluctuation in real time when weaving the Kasuri and keep it within the acceptable range. This study proposes a method for maintaining a unique faded pattern of woven textiles by utilizing EfficientNet for classification, fine-tuned with Optuna, and LightGBM for predicting subtle misalignments. Our experiments show that EfficientNet achieves high performance in classifying the quality of unique faded patterns in woven textiles. Additionally, LightGBM demonstrates near-perfect accuracy in predicting subtle misalignments within the acceptable range for high-quality faded patterns by controlling the weaving thread tension. Consequently, this method effectively maintains the quality of Kurume Kasuri patterns within the desired criteria. Full article
(This article belongs to the Special Issue AI-Based Image Processing and Computer Vision)
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