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The State of the Art of Computer Vision and Pattern Recognition, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 4114

Special Issue Editors

Prof. Dr. Hyeonjoon Moon

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: big data; computer vision; pattern recognition; biometrics; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Lien Minh Dang

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea
Interests: deep learning; object detection; NLP; pattern recognition; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

After the Special Issue entitled “State of the Art of Computer Vision and Pattern Recognition”, we have decided to publish a second edition that will unite specialists in the field and showcase the latest advancements and findings related to the topic.

In the rapidly evolving field of computer vision and pattern recognition, continuous advancements are reshaping the way we perceive and interact with visual data. This Special Issue aims to discuss the latest breakthroughs and innovations in these domains, offering a comprehensive snapshot of the cutting-edge research that is pushing the boundaries of what is possible.

This Special Issue will cover a wide spectrum of topics, including, but not limited to, image classification, object detection, image segmentation, video analysis, deep learning, feature extraction, face recognition, and gesture recognition. Contributions will explore novel algorithms, architectures, methodologies, and applications that contribute to the enhanced understanding and interpretation of visual data. Additionally, this Special Issue will explore the combination of computer vision and pattern recognition, highlighting the synergies between these two fields and their combined potential to revolutionize various industries.

We invite researchers, practitioners, and experts in computer vision and pattern recognition to submit their original research, reviews, and case studies. This Special Issue aims to foster interdisciplinary collaboration, enabling researchers to share their insights, experiences, and challenges. By addressing both theoretical and practical aspects, this collection of articles will not only provide a comprehensive overview of recent advances but also serve as a valuable resource for researchers, practitioners, and educators in the field.

Prof. Dr. Hyeonjoon Moon
Dr. Lien Minh Dang
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. Applied Sciences 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

  • image classification
  • object detection
  • image segmentation
  • video analysis
  • deep learning
  • feature extraction
  • gesture recognition
  • pattern recognition
  • computer vision
  • pattern recognition
  • face recognition

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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25 pages, 12242 KiB  
Article
ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images
by Gustavo Zanoni Felipe, Loris Nanni, Isadora Goulart Garcia, Jacqueline Nelisis Zanoni and Yandre Maldonado e Gomes da Costa
Appl. Sci. 2025, 15(3), 1046; https://doi.org/10.3390/app15031046 - 21 Jan 2025
Viewed by 961
Abstract
The Enteric Nervous System (ENS) is a dynamic field of study where researchers devise sophisticated methodologies to comprehend the impact of chronic degenerative diseases on Enteric Neuron Cells (ENCs). These investigations demand labor-intensive effort, requiring manual selection and segmentation of each well-defined cell [...] Read more.
The Enteric Nervous System (ENS) is a dynamic field of study where researchers devise sophisticated methodologies to comprehend the impact of chronic degenerative diseases on Enteric Neuron Cells (ENCs). These investigations demand labor-intensive effort, requiring manual selection and segmentation of each well-defined cell to conduct morphometric and quantitative analyses. However, the scarcity of labeled data and the unique characteristics of such data limit the applicability of existing solutions in the literature. To address this, we introduce a novel dataset featuring expert-labeled ENC called ENSeg, which comprises 187 images and 9709 individually annotated cells. We also introduce an approach that combines automatic instance segmentation models with Segment Anything Model (SAM) architectures, enabling human interaction while maintaining high efficiency. We employed YOLOv8, YOLOv9, and YOLOv11 models to generate segmentation candidates, which were then integrated with SAM architectures through a fusion protocol. Our best result achieved a mean DICE score (mDICE) of 0.7877, using YOLOv8 (candidate selection), SAM, and a fusion protocol that enhanced the input point prompts. The resulting combination protocols, demonstrated after our work, exhibit superior segmentation performance compared to the standalone segmentation models. The dataset comes as a contribution to this work and is available to the research community. Full article
(This article belongs to the Special Issue The State of the Art of Computer Vision and Pattern Recognition, 2nd Edition)
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Review

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37 pages, 8629 KiB  
Review
A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
by Mohamed Mahmoud, Mahmoud SalahEldin Kasem and Hyun-Soo Kang
Appl. Sci. 2024, 14(19), 8781; https://doi.org/10.3390/app14198781 - 28 Sep 2024
Cited by 4 | Viewed by 2509
Abstract
Masked face recognition (MFR) has emerged as a critical domain in biometric identification, especially with the global COVID-19 pandemic, which introduced widespread face masks. This survey paper presents a comprehensive analysis of the challenges and advancements in recognizing and detecting individuals with masked [...] Read more.
Masked face recognition (MFR) has emerged as a critical domain in biometric identification, especially with the global COVID-19 pandemic, which introduced widespread face masks. This survey paper presents a comprehensive analysis of the challenges and advancements in recognizing and detecting individuals with masked faces, which has seen innovative shifts due to the necessity of adapting to new societal norms. Advanced through deep learning techniques, MFR, along with face mask recognition (FMR) and face unmasking (FU), represents significant areas of focus. These methods address unique challenges posed by obscured facial features, from fully to partially covered faces. Our comprehensive review explores the various deep learning-based methodologies developed for MFR, FMR, and FU, highlighting their distinctive challenges and the solutions proposed to overcome them. Additionally, we explore benchmark datasets and evaluation metrics specifically tailored for assessing performance in MFR research. The survey also discusses the substantial obstacles still facing researchers in this field and proposes future directions for the ongoing development of more robust and effective masked face recognition systems. This paper serves as an invaluable resource for researchers and practitioners, offering insights into the evolving landscape of face recognition technologies in the face of global health crises and beyond. Full article
(This article belongs to the Special Issue The State of the Art of Computer Vision and Pattern Recognition, 2nd Edition)
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