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Artificial Intelligence in Computer Vision: Methods and Applications—2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 25 February 2025 | Viewed by 2361

Special Issue Editors

Dr. Zhaoyang Wang

E-Mail Website
Guest Editor
Department of Mechanical Engineering, The Catholic University of America, Washington, DC 20064, USA
Interests: optics; mechanics; robotics; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Minh P. Vo

E-Mail Website
Guest Editor
Spree3D, Alameda, CA 94502, USA
Interests: computer vision; computational photography; machine learning
Dr. Hieu Nguyen

E-Mail Website
Guest Editor
Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD 21224, USA
Interests: computer vision; machine learning; deep learning; computer hardware; neuroimaging
Special Issues, Collections and Topics in MDPI journals
Dr. John Hyatt

E-Mail Website
Guest Editor
U.S. Army Research Laboratory, 2201 Aberdeen Boulevard, Aberdeen, MD 21005, USA
Interests: machine learning

Special Issue Information

Dear Colleagues,

In recent years, there has been high interest in the research and development of artificial intelligence techniques. In the meantime, computer vision methods have been enhanced and extended to encompass an astonishing number of novel sensors and measurement systems. As artificial intelligence spreads over almost all fields of science and engineering, computer vision remains one of its primary application areas. Notably, incorporating artificial intelligence into computer vision-based sensing and measurement techniques has led to numerous unprecedented performances, such as high-accuracy object detection, image segmentation, human pose estimation, and real-time 3D sensing, which cannot be fulfilled using conventional methods.

This Special Issue aims to cover the recent advancements in computer vision that involve using artificial intelligence methods, with a particular interest in sensors and sensing. Both original research and review articles are welcome. Typical topics include but are not limited to the following:

  • Physical, chemical, biological, and healthcare sensors and sensing techniques with deep learning approaches;
  • Localization, mapping, and navigation techniques with artificial intelligence;
  • Artificial intelligence-based recognition of objects, scenes, actions, faces, gestures, expressions, and emotions, as well as object relations and interactions;
  • 3D imaging and sensing with deep learning schemes;
  • Accurate learning with simulation datasets or with a small number of training labels for sensors and sensing;
  • Supervised and unsupervised learning for sensors and sensing;
  • Broad computer vision methods and applications that involve using deep learning or artificial intelligence.

Dr. Zhaoyang Wang
Dr. Minh P. Vo
Dr. Hieu Nguyen
Dr. John Hyatt
Guest Editors

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. Sensors 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

  • artificial intelligence
  • deep learning
  • computer vision
  • smart sensors
  • intelligent sensing
  • 3D imaging and sensing
  • localization and mapping
  • navigation and positioning

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Related Special Issue

Published Papers (2 papers)

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Research

22 pages, 12107 KiB  
Article
Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification
by Sifa Ozsari, Eda Kumru, Fatih Ekinci, Ilgaz Akata, Mehmet Serdar Guzel, Koray Acici, Eray Ozcan and Tunc Asuroglu
Sensors 2024, 24(22), 7189; https://doi.org/10.3390/s24227189 - 9 Nov 2024
Viewed by 630
Abstract
This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as Amanita pantherina, Boletus edulis, Cantharellus cibarius, Lactarius deliciosus, Pleurotus ostreatus and Tricholoma terreum were chosen based on their ecological [...] Read more.
This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as Amanita pantherina, Boletus edulis, Cantharellus cibarius, Lactarius deliciosus, Pleurotus ostreatus and Tricholoma terreum were chosen based on their ecological importance and distinct morphological characteristics. The research employed 5 different machine learning techniques and 12 deep learning models, including DenseNet121, MobileNetV2, ConvNeXt, EfficientNet, and swin transformers, to evaluate their performance in identifying fungi from images. The DenseNet121 model demonstrated the highest accuracy (92%) and AUC score (95%), making it the most effective in distinguishing between species. The study also revealed that transformer-based models, particularly the swin transformer, were less effective, suggesting room for improvement in their application to this task. Further advancements in macrofungi classification could be achieved by expanding datasets, incorporating additional data types such as biochemical, electron microscopy, and RNA/DNA sequences, and using ensemble methods to enhance model performance. The findings contribute valuable insights into both the use of deep learning for biodiversity research and the ecological conservation of macrofungi species. Full article
(This article belongs to the Special Issue Artificial Intelligence in Computer Vision: Methods and Applications—2nd Edition)
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23 pages, 1025 KiB  
Article
Adversarial Examples on XAI-Enabled DT for Smart Healthcare Systems
by Niddal H. Imam
Sensors 2024, 24(21), 6891; https://doi.org/10.3390/s24216891 - 27 Oct 2024
Viewed by 703
Abstract
There have recently been rapid developments in smart healthcare systems, such as precision diagnosis, smart diet management, and drug discovery. These systems require the integration of the Internet of Things (IoT) for data acquisition, Digital Twins (DT) for data representation into a digital [...] Read more.
There have recently been rapid developments in smart healthcare systems, such as precision diagnosis, smart diet management, and drug discovery. These systems require the integration of the Internet of Things (IoT) for data acquisition, Digital Twins (DT) for data representation into a digital replica and Artificial Intelligence (AI) for decision-making. DT is a digital copy or replica of physical entities (e.g., patients), one of the emerging technologies that enable the advancement of smart healthcare systems. AI and Machine Learning (ML) offer great benefits to DT-based smart healthcare systems. They also pose certain risks, including security risks, and bring up issues of fairness, trustworthiness, explainability, and interpretability. One of the challenges that still make the full adaptation of AI/ML in healthcare questionable is the explainability of AI (XAI) and interpretability of ML (IML). Although the study of the explainability and interpretability of AI/ML is now a trend, there is a lack of research on the security of XAI-enabled DT for smart healthcare systems. Existing studies limit their focus to either the security of XAI or DT. This paper provides a brief overview of the research on the security of XAI-enabled DT for smart healthcare systems. It also explores potential adversarial attacks against XAI-enabled DT for smart healthcare systems. Additionally, it proposes a framework for designing XAI-enabled DT for smart healthcare systems that are secure and trusted. Full article
(This article belongs to the Special Issue Artificial Intelligence in Computer Vision: Methods and Applications—2nd Edition)
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