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AI-Based Image Processing and Computer Vision
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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 December 2025 | Viewed by 4688

Special Issue Editor

Prof. Dr. Kohei Arai

E-Mail Website
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

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. Information is an international peer-reviewed open access monthly 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 1600 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
  • pattern analysis
  • machine intelligence
  • pattern recognition
  • image understanding

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Published Papers (6 papers)

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Research

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24 pages, 10115 KiB  
Article
iSight: A Smart Clothing Management System to Empower Blind and Visually Impaired Individuals
by Daniel Rocha, Celina P. Leão, Filomena Soares and Vítor Carvalho
Information 2025, 16(5), 383; https://doi.org/10.3390/info16050383 - 3 May 2025
Viewed by 268
Abstract
Clothing management is a major challenge for blind and visually impaired individuals to perform independently. This research developed and validated the iSight, a mechatronic smart wardrobe prototype, integrating computer vision and artificial intelligence to identify clothing types, colours, and alterations. Tested with 15 [...] Read more.
Clothing management is a major challenge for blind and visually impaired individuals to perform independently. This research developed and validated the iSight, a mechatronic smart wardrobe prototype, integrating computer vision and artificial intelligence to identify clothing types, colours, and alterations. Tested with 15 participants, iSight achieved high user satisfaction, with 60% rating it as very accurate in clothing identification, 80% in colour detection, and 86.7% in near-field communication tag recognition. Statistical analyses confirmed its positive impact on confidence, independence, and well-being. Despite the fact that improvements in menu complexity and fabric information were suggested, iSight proves to be a robust, user-friendly assistive tool with the potential to enhance the daily living of blind and visually impaired individuals. Full article
(This article belongs to the Special Issue AI-Based Image Processing and Computer Vision)
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15 pages, 5939 KiB  
Article
Center-Guided Network with Dynamic Attention for Transmission Tower Detection
by Xiaobin Li, Zhuwei Liang, Jingbin Yang, Chuanlong Lyu and Yuge Xu
Information 2025, 16(4), 331; https://doi.org/10.3390/info16040331 - 21 Apr 2025
Viewed by 147
Abstract
Transmission tower detection in aerial images is the critical step for the inspection of power transmission equipment, which is essential for the stable operation of the power system. However, transmission towers in aerial images pose numerous challenges for object detection due to their [...] Read more.
Transmission tower detection in aerial images is the critical step for the inspection of power transmission equipment, which is essential for the stable operation of the power system. However, transmission towers in aerial images pose numerous challenges for object detection due to their multi-scale elongated shapes, large aspect ratios, and visually similar backgrounds. To address these problems, we propose the Center-Guided network with Dynamic Attention (CGDA) for detecting TTs from aerial images. Specifically, we apply ResNet and FPN as the feature extractor to extract high-quality and multi-scale features. To obtain more discriminative information, the dynamic attention mechanism is employed to dynamically fuse multi-scale feature maps and place more attention on the object regions. In addition, a two-stage detection head is proposed to employ a two-stage detection process to perform more accurate detection. Extensive experiments are conducted on a subset of the public TTPLA dataset. The results show that CGDA achieves competitive performance in detecting TTs, demonstrating the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue AI-Based Image Processing and Computer Vision)
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21 pages, 5371 KiB  
Article
From Pixels to Diagnosis: Implementing and Evaluating a CNN Model for Tomato Leaf Disease Detection
by Zamir Osmenaj, Evgenia-Maria Tseliki, Sofia H. Kapellaki, George Tselikis and Nikolaos D. Tselikas
Information 2025, 16(3), 231; https://doi.org/10.3390/info16030231 - 16 Mar 2025
Viewed by 914
Abstract
The frequent emergence of multiple diseases in tomato plants poses a significant challenge to agriculture, requiring innovative solutions to deal with this problem. The paper explores the application of machine learning (ML) technologies to develop a model capable of identifying and classifying diseases [...] Read more.
The frequent emergence of multiple diseases in tomato plants poses a significant challenge to agriculture, requiring innovative solutions to deal with this problem. The paper explores the application of machine learning (ML) technologies to develop a model capable of identifying and classifying diseases in tomato leaves. Our work involved the implementation of a custom convolutional neural network (CNN) trained on a diverse dataset of tomato leaf images. The performance of the proposed CNN model was evaluated and compared against the performance of existing pre-trained CNN models, i.e., the VGG16 and VGG19 models, which are extensively used for image classification tasks. The proposed CNN model was further tested with images of tomato leaves captured from a real-world garden setting in Greece. The captured images were carefully preprocessed and an in-depth study was conducted on how either each image preprocessing step or a different—not supported by the dataset used—strain of tomato affects the accuracy and confidence in detecting tomato leaf diseases. Full article
(This article belongs to the Special Issue AI-Based Image Processing and Computer Vision)
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15 pages, 3085 KiB  
Article
Early Detection of Skin Diseases Across Diverse Skin Tones Using Hybrid Machine Learning and Deep Learning Models
by Akasha Aquil, Faisal Saeed, Souad Baowidan, Abdullah Marish Ali and Nouh Sabri Elmitwally
Information 2025, 16(2), 152; https://doi.org/10.3390/info16020152 - 19 Feb 2025
Viewed by 1362
Abstract
Skin diseases in melanin-rich skin often present diagnostic challenges due to the unique characteristics of darker skin tones, which can lead to misdiagnosis or delayed treatment. This disparity impacts millions within diverse communities, highlighting the need for accurate, AI-based diagnostic tools. In this [...] Read more.
Skin diseases in melanin-rich skin often present diagnostic challenges due to the unique characteristics of darker skin tones, which can lead to misdiagnosis or delayed treatment. This disparity impacts millions within diverse communities, highlighting the need for accurate, AI-based diagnostic tools. In this paper, we investigated the performance of three machine learning methods -Support Vector Machines (SVMs), Random Forest (RF), and Decision Trees (DTs)-combined with state-of-the-art (SOTA) deep learning models, EfficientNet, MobileNetV2, and DenseNet121, for predicting skin conditions using dermoscopic images from the HAM10000 dataset. The features were extracted using the deep learning models, with the labels encoded numerically. To address the data imbalance, SMOTE and resampling techniques were applied. Additionally, Principal Component Analysis (PCA) was used for feature reduction, and fine-tuning was performed to optimize the models. The results demonstrated that RF with DenseNet121 achieved a superior accuracy of 98.32%, followed by SVM with MobileNetV2 at 98.08%, and Decision Tree with MobileNetV2 at 85.39%. The proposed methods overcome the SVM with the SOTA EfficientNet model, validating the robustness of the proposed approaches. Evaluation metrics such as accuracy, precision, recall, and F1-score were used to benchmark performance, showcasing the potential of these methods in advancing skin disease diagnostics for diverse populations. Full article
(This article belongs to the Special Issue AI-Based Image Processing and Computer Vision)
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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
Cited by 1 | Viewed by 1025
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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Review

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29 pages, 2763 KiB  
Review
A Review of Computer Vision Technology for Football Videos
by Fucheng Zheng, Duaa Zuhair Al-Hamid, Peter Han Joo Chong, Cheng Yang and Xue Jun Li
Information 2025, 16(5), 355; https://doi.org/10.3390/info16050355 - 28 Apr 2025
Viewed by 370
Abstract
In the era of digital advancement, the integration of Deep Learning (DL) algorithms is revolutionizing performance monitoring in football. Due to restrictions on monitoring devices during games to prevent unfair advantages, coaches are tasked to analyze players’ movements and performance visually. As a [...] Read more.
In the era of digital advancement, the integration of Deep Learning (DL) algorithms is revolutionizing performance monitoring in football. Due to restrictions on monitoring devices during games to prevent unfair advantages, coaches are tasked to analyze players’ movements and performance visually. As a result, Computer Vision (CV) technology has emerged as a vital non-contact tool for performance analysis, offering numerous opportunities to enhance the clarity, accuracy, and intelligence of sports event observations. However, existing CV studies in football face critical challenges, including low-resolution imagery of distant players and balls, severe occlusion in crowded scenes, motion blur during rapid movements, and the lack of large-scale annotated datasets tailored for dynamic football scenarios. This review paper fills this gap by comprehensively analyzing advancements in CV, particularly in four key areas: player/ball detection and tracking, motion prediction, tactical analysis, and event detection in football. By exploring these areas, this review offers valuable insights for future research on using CV technology to improve sports performance. Future directions should prioritize super-resolution techniques to enhance video quality and improve small-object detection performance, collaborative efforts to build diverse and richly annotated datasets, and the integration of contextual game information (e.g., score differentials and time remaining) to improve predictive models. The in-depth analysis of current State-Of-The-Art (SOTA) CV techniques provides researchers with a detailed reference to further develop robust and intelligent CV systems in football. Full article
(This article belongs to the Special Issue AI-Based Image Processing and Computer Vision)
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