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Electronics
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Applications of Artificial Intelligence in Computer Vision, 2nd Edition
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Applications of Artificial Intelligence in Computer Vision, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 October 2025) | Viewed by 2647

Special Issue Editor

Prof. Dr. Jenhui Chen

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan City 33302, Taiwan
Interests: multimedia; computer vision; Internet of Things; financial technology; knowledge graph; medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit your research contributions to the upcoming Special Issue on "Applications of Artificial Intelligence in Computer Vision, 2nd Edition". This Special Issue aims to bring together leading researchers and practitioners from academia and industry to discuss the latest advances, findings, and practical applications of AI in the field of computer vision.

Scope and Topics

The broad and interdisciplinary nature of artificial intelligence (AI) in computer vision makes it an engaging and impactful area for research. This Special Issue invites high-quality, original, and previously unpublished research papers, reviews, and case studies that contribute to this growing field. Topics of interest for submission include, but are not limited to, the following:

  1. Advanced machine learning and deep learning techniques for image and video analyses;
  2. Object detection, recognition, and tracking;
  3. Scene understanding and semantic segmentation;
  4. Three-dimensional vision and depth estimation;
  5. Face and gesture recognition;
  6. AI in medical image analyses;
  7. Augmented reality (AR) and virtual reality (VR) in computer vision;
  8. AI-driven surveillance and security systems;
  9. Real-time computer vision applications;
  10. Explainable AI in computer vision.

Prof. Dr. Jenhui Chen
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 250 words) can be sent to the Editorial Office for assessment.

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

  • computer vision
  • image processing
  • medical image
  • object detection
  • scene understanding

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Related Special Issue

Published Papers (2 papers)

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Research

13 pages, 4935 KB  
Article
Enhancing Self-Driving Segmentation in Adverse Weather Conditions: A Dual Uncertainty-Aware Training Approach to SAM Optimization
by Zhuoyuan Cao, Kevin Wang, Saleh Abdelrahman, Jeffery Wu and Dharsan Ravindran
Electronics 2025, 14(23), 4555; https://doi.org/10.3390/electronics14234555 - 21 Nov 2025
Viewed by 457
Abstract
Recent advancements in vision foundation models, such as the Segment Anything Model (SAM) and its successor SAM2, have established new state-of-the-art benchmarks for image segmentation tasks. However, these models often fail in inclement weather scenarios where visual ambiguity is prevalent, primarily due to [...] Read more.
Recent advancements in vision foundation models, such as the Segment Anything Model (SAM) and its successor SAM2, have established new state-of-the-art benchmarks for image segmentation tasks. However, these models often fail in inclement weather scenarios where visual ambiguity is prevalent, primarily due to their lack of uncertainty quantification capabilities. Drawing inspiration from recent successes in medical imaging—where uncertainty-aware training has shown considerable promise in handling ambiguous cases—we explore two approaches to enhance segmentation performance in adverse driving conditions. First, we implement a multistep fine-tuning process for SAM2 that incorporates uncertainty metrics directly into the loss function to improve overall scene recognition. Second, we adapt the Uncertainty-Aware Adapter (UAT), originally developed for medical image segmentation, to autonomous driving contexts. We evaluate these approaches on the CamVid and BDD100K datasets, while the GTA Driving dataset is used exclusively during the fine-tuning process for adaptation and not for evaluation, helping improve generalization to diverse driving conditions. Our experimental results demonstrate that UAT-SAM improves IoU by 42.7% and Dice by 30% under heavy-weather conditions, while the fine-tuned SAM2 with uncertainty-aware loss shows improved performance across a wide range of driving scenes. These findings highlight the importance of explicit uncertainty modeling in safety-critical autonomous driving applications, particularly when operating in challenging environmental conditions. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Computer Vision, 2nd Edition)
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22 pages, 9206 KB  
Article
An Enhanced Multiscale Retinex, Oriented FAST and Rotated BRIEF (ORB), and Scale-Invariant Feature Transform (SIFT) Pipeline for Robust Key Point Matching in 3D Monitoring of Power Transmission Line Icing with Binocular Vision
by Nalini Rizkyta Nusantika, Jin Xiao and Xiaoguang Hu
Electronics 2024, 13(21), 4252; https://doi.org/10.3390/electronics13214252 - 30 Oct 2024
Cited by 7 | Viewed by 1586
Abstract
Power transmission line icing (PTLI) poses significant threats to the reliability and safety of electrical power systems, particularly in cold regions. Accumulation of ice on power lines can lead to severe consequences, such as line breaks, tower collapses, and widespread power outages, resulting [...] Read more.
Power transmission line icing (PTLI) poses significant threats to the reliability and safety of electrical power systems, particularly in cold regions. Accumulation of ice on power lines can lead to severe consequences, such as line breaks, tower collapses, and widespread power outages, resulting in economic losses and infrastructure damage. This study proposes an enhanced image processing pipeline to accurately detect and match key points in PTLI images for 3D monitoring of ice thickness using binocular vision. The pipeline integrates established techniques such as multiscale retinex (MSR), oriented FAST and rotated BRIEF (ORB) and scale-invariant feature transform (SIFT) algorithms, further refined with m-estimator sample consensus (MAGSAC)-based random sampling consensus (RANSAC) optimization. The image processing steps include automatic cropping, image enhancement, feature detection, and robust key point matching, all designed to operate in challenging environments with poor lighting and noise. Experiments demonstrate that the proposed method significantly improves key point matching accuracy and computational efficiency, reducing processing time to make it suitable for real-time applications. The effectiveness of the pipeline is validated through 3D ice thickness measurements, with results showing high precision and low error rates, making it a valuable tool for monitoring power transmission lines in harsh conditions. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Computer Vision, 2nd Edition)
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