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Electronics
Special Issues
Recent Trends in Image Processing and Pattern Recognition
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Recent Trends in Image Processing and Pattern Recognition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (7 March 2024) | Viewed by 5494

Special Issue Editors

Prof. Dr. KC Santosh

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Guest Editor
Applied AI Research Lab, Department of Computer Science, The University of South Dakota, Vermillion, SD 57069, USA
Interests: AI; machine learning; computer vision; pattern recognition; biomedical imaging; healthcare informatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Myra Conway

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Guest Editor
HAS-Applied Sciences, University of the West of England, Bristol BS16 1QY, UK
Interests: biomedical imaging; healthcare informatics
Prof. Dr. Ashutosh Kumar Singh

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Guest Editor
Indian Institute of Information Technology, Bhopal 462003, Madhya Pradesh, India
Interests: cloud computing; quantum cellular automata; reversible logic
Dr. Aaisha Makkar

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Guest Editor
College of Science and Engineering, University of Derby, Derby DE22 1GB, UK
Interests: machine learning; federated learning; IOT; security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antoine Vacavant

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Guest Editor
Institut Pascal, Université Clermont Auvergne, CNRS, SIGMA Clermont, F-63000 Clermont-Ferrand, France
Interests: medical and biomedical image analysis; robustness for image processing; computer vision; machine learning; discrete mathematical models (geometry, topology, morphology); benchmarking and evaluation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anas Abou El Kalam

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Guest Editor
Department of Computer Science, Cadi Ayyad University, Marrakesh 40 001, Morocco
Interests: security; cybersecurity
Dr. Mohamed-Rafik Bouguelia

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Guest Editor
Högskolan i Halmstad, Halmstad, Sweden
Interests: machine learning; data mining; artificial intelligence; data science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ravindra Hegadi

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Guest Editor
Department of Computer Science, Central Univ of Karnataka, Karnataka 585367, India
Interests: digital image processing; medical image analysis; document image analysis; biometrics; machine vision; robotics

Special Issue Information

Dear Colleagues,

The 6th International Conference on Recent Trends in Image Processing and Pattern Recognition (RTIP2R) aims to attract current and/or advanced research on image processing, pattern recognition, computer vision, and machine learning. The RTIP2R will take place at the University of Derby, United Kingdom on December 07–08, 2023 in collaboration with the 2AI Research Lab—Computer Science, University of South Dakota (USA).

Authors of selected papers from the conference will be invited to submit extended versions of their original papers and contributions under the conference topics (new papers that are closely related to the conference themes are also welcome).

We, however, are not limited to RIP2R 2023 to increase the number of submissions.

Topics of interest include, but are not limited to, the following:

  • Signal and image processing.
  • Computer vision and pattern recognition: object detection and/or recognition (shape, color, and texture analysis) as well as pattern recognition (statistical, structural, and syntactic methods).
  • Machine learning: algorithms, clustering and classification, model selection (machine learning), feature engineering, and deep learning.
  • Data analytics: data mining tools and high-performance computing in big data.
  • Federated learning: applications and challenges.
  • Pattern recognition and machine learning for the Internet of things (IoT).
  • Information retrieval: content-based image retrieval and indexing, as well as text analytics.
  • Applications (not limited to):
    • Document image analysis and understanding.
    • Forensics.
    • Biometrics: face matching, iris recognition/verification, footprint verification, and audio/speech analysis, as well as understanding.
    • Healthcare informatics and (bio)medical imaging, as well as engineering.
    • Big data (from document understanding and healthcare to risk management).
    • Cryptanalysis (cryptology and cryptography).

Prof. Dr. KC Santosh
Prof. Dr. Myra Conway
Prof. Dr. Ashutosh Kumar Singh
Dr. Aaisha Makkar
Prof. Dr. Antoine Vacavant
Prof. Dr. Anas Abou El Kalam
Dr. Mohamed-Rafik Bouguelia
Prof. Dr. Ravindra Hegadi
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. 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.

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

Published Papers (3 papers)

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Research

14 pages, 1486 KiB  
Article
Comparative Study of Adversarial Defenses: Adversarial Training and Regularization in Vision Transformers and CNNs
by Hiskias Dingeto and Juntae Kim
Electronics 2024, 13(13), 2534; https://doi.org/10.3390/electronics13132534 - 27 Jun 2024
Viewed by 1090
Abstract
Transformer-based models are driving a significant revolution in the field of machine learning at the moment. Among these innovations, vision transformers (ViTs) stand out for their application of transformer architectures to vision-related tasks. By demonstrating performance as good, if not better, than traditional [...] Read more.
Transformer-based models are driving a significant revolution in the field of machine learning at the moment. Among these innovations, vision transformers (ViTs) stand out for their application of transformer architectures to vision-related tasks. By demonstrating performance as good, if not better, than traditional convolutional neural networks (CNNs), ViTs have managed to capture considerable interest in the field. This study focuses on the resilience of ViTs and CNNs in the face of adversarial attacks. Such attacks, which introduce noise into the input of machine learning models to produce incorrect outputs, pose significant challenges to the reliability of machine learning models. Our analysis evaluated the adversarial robustness of CNNs and ViTs by using regularization techniques and adversarial training methods. Adversarial training, in particular, represents a traditional approach to boosting defenses against these attacks. Despite its prominent use, our findings reveal that regularization techniques enable vision transformers and, in most cases, CNNs to enhance adversarial defenses more effectively. Through testing datasets like CIFAR-10 and CIFAR-100, we demonstrate that vision transformers, especially when combined with effective regularization strategies, demonstrate adversarial robustness, even without adversarial training. Two main inferences can be drawn from our findings. Firstly, it emphasizes how effectively vision transformers could strengthen artificial intelligence defenses against adversarial attacks. Secondly, it shows how regularization, which requires much fewer computational resources and covers a wide range of adversarial attacks, can be effective for adversarial defenses. Understanding and improving a model’s resilience to adversarial attacks is crucial for developing secure, dependable systems that can handle the complexity of real-world applications as artificial intelligence and machine learning technologies advance. Full article
(This article belongs to the Special Issue Recent Trends in Image Processing and Pattern Recognition)
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21 pages, 5251 KiB  
Article
Cross-Scene Hyperspectral Image Classification Based on Graph Alignment and Distribution Alignment
by Haisong Chen, Shanshan Ding and Aili Wang
Electronics 2024, 13(9), 1731; https://doi.org/10.3390/electronics13091731 - 1 May 2024
Viewed by 854
Abstract
A domain alignment-based hyperspectral image (HSI) classification method was designed to address the heterogeneity in resolution and band between the source domain and target domain datasets of cross-scene hyperspectral images, as well as the resulting reduction in common features. Firstly, after preliminary feature [...] Read more.
A domain alignment-based hyperspectral image (HSI) classification method was designed to address the heterogeneity in resolution and band between the source domain and target domain datasets of cross-scene hyperspectral images, as well as the resulting reduction in common features. Firstly, after preliminary feature extraction, perform two domain alignment operations: image alignment and distribution alignment. Image alignment aims to align hyperspectral images of different bands or time points, ensuring that they are within the same spatial reference framework. Distribution alignment adjusts the distribution of features of samples of different categories in the feature space to reduce the distribution differences of the same type of features between two domains. Secondly, adjust the consistency of the two alignment methods to ensure that the features obtained through different alignment methods exhibit consistency in the feature space, thereby improving the comparability and reliability of the features. In addition, this method considers multiple losses in the model from different perspectives and makes comprehensive adjustments through a unified optimization process to more comprehensively capture and utilize the correlation information between data. Experimental results on Houston 2013 and Houston 2018 datasets can improve the hyperspectral prediction performance between datasets with different resolutions and bands, effectively solving the problems of high cost and limited training samples in HSI labeling and significantly improving cross-scene HSI classification performance. Full article
(This article belongs to the Special Issue Recent Trends in Image Processing and Pattern Recognition)
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15 pages, 6045 KiB  
Article
Lightweight DB-YOLO Facemask Intelligent Detection and Android Application Based on Bidirectional Weighted Feature Fusion
by Bin Qin, Ying Zeng, Xin Wang, Junmin Peng, Tao Li, Teng Wang and Yuxin Qin
Electronics 2023, 12(24), 4936; https://doi.org/10.3390/electronics12244936 - 8 Dec 2023
Cited by 2 | Viewed by 1337
Abstract
Conventional facemask detection algorithms face challenges of insufficient accuracy, large model size, and slow computation speed, limiting their deployment in real-world scenarios, especially on edge devices. Aiming at addressing these issues, we proposed a DB-YOLO facemask intelligent detection algorithm, which is a lightweight [...] Read more.
Conventional facemask detection algorithms face challenges of insufficient accuracy, large model size, and slow computation speed, limiting their deployment in real-world scenarios, especially on edge devices. Aiming at addressing these issues, we proposed a DB-YOLO facemask intelligent detection algorithm, which is a lightweight solution that leverages bidirectional weighted feature fusion. Our method is built on the YOLOv5 algorithm model, replacing the original YOLOv5 backbone network with the lightweight ShuffleNetv2 to reduce parameters and computational requirements. Additionally, we integrated BiFPN as the feature fusion layer, enhancing the model’s detection capability for objects of various scales. Furthermore, we employed a CARAFE lightweight upsampling factor to improve the model’s perception of details and small-sized objects and the EIOU loss function to expedite model convergence. We validated the effectiveness of our proposed method through experiments conducted on the Pascal VOC2007+2012 and Face_Mask datasets. Our experimental results demonstrate that the DB-YOLO model boasts a compact size of approximately 1.92 M. It achieves average precision values of 70.1% and 93.5% on the Pascal VOC2007+2012 and Face_Mask datasets, respectively, showcasing a 2.3% improvement in average precision compared to the original YOLOv5s. Furthermore, the model’s size is reduced by 85.8%. We also successfully deployed the model on Android devices using the NCNN framework, achieving a detection speed of up to 33 frames per second. Compared to lightweight algorithm models like YOLOv5n, YOLOv4-Tiny, and YOLOv3-Tiny, DB-YOLO not only reduces the model’s size but also effectively improves detection accuracy, exhibiting excellent practicality and promotional value on edge devices. Full article
(This article belongs to the Special Issue Recent Trends in Image Processing and Pattern Recognition)
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