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Electronics
Special Issues
Computer Vision Imaging Technology and Application
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Computer Vision Imaging Technology and Application

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

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 6036

Special Issue Editors

Dr. Alexandre Meyer

E-Mail Website
Guest Editor
Department of Computer Science/LIRIS, Université Claude Bernard Lyon 1, F-69100 Lyon, France
Interests: computer vision; computer animation; motion analysis; expression recognition; easy motion capture; virtual reality
Prof. Dr. Rizwan Ahmed Khan

E-Mail Website
Guest Editor
Faculty of Information Technology, Salim Habib University (Formerly Barrett Hodgson University), NC-24, Deh Dih, Korangi Creek, Karachi 74900, Pakistan
Interests: computer vision; machine learning; medical image analysis; explainable AI (XAI)
Prof. Dr. Xiangjian He

E-Mail Website
Co-Guest Editor
Computer Vision and Intelligent Perception Lab, School of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China
Interests: deep-learning-based research for human behavior recognition; human counting and density estimation; tiny object detection; biomedical applications; saliency detection; natural language processing; cybersecurity; face and face expression recognition; road sign detection; license plate recognition

Special Issue Information

Dear Colleagues,

The objective of this Special Issue is to discuss the latest innovations in computer vision and image processing technologies, with a focus on applications and software development. Computer vision is an area of computer science and electronics that uses machine learning to enable computers to see, recognize, and analyze often moving objects in photos and videos. Advances in computer vision can be achieved by working on the algorithms in general, but also on the hardware, especially when the targeted application needs real time.

The aim of this Special Issue on “Computer Vision Imaging Technology and Application” is to bring together the research communities interested in computer vision from various fields, such as electronics, robotics, and computer science, with a special focus on innovative applications in various domains, such as virtual reality, video games, medicine, industry 4.0, agriculture, transportation, sports, retail, etc.

The topics of interest include but are not limited to the following:

  • Image processing and computer vision;
  • Artificial intelligence systems for computer vision;
  • FPGA or GPU-based acceleration of computer vision algorithms;
  • Low power computer vision and deep learning;
  • Embedded vision and deep learning systems;
  • Vision sensor systems;
  • Optimizing material for computer vision;
  • Efficient implementation of computer vision algorithm in specific setups;
  • Smart camera systems;
  • Applications using neuromorphic cameras;
  • Hardware setup for applications of computer vision in virtual reality, games, medicine, industry, agriculture, transportation, sport, retail, etc.

We look forward to receiving your contributions.

Dr. Alexandre Meyer
Prof. Dr. Rizwan Ahmed Khan
Prof. Dr. Xiangjian He
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.

Keywords

  • image processing
  • computer vision
  • embedded vision
  • vision sensor systems

Published Papers (3 papers)

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Research

16 pages, 900 KiB  
Article
Recognition of Children’s Facial Expressions Using Deep Learned Features
by Unqua Laraib, Arslan Shaukat, Rizwan Ahmed Khan, Zartasha Mustansar, Muhammad Usman Akram and Umer Asgher
Electronics 2023, 12(11), 2416; https://doi.org/10.3390/electronics12112416 - 26 May 2023
Cited by 3 | Viewed by 1786
Abstract
The emotional well-being of a child is crucial for their successful integration into society as a productive individual. While technology has made significant strides in enabling machines to decipher human emotional signals, current research in emotion recognition primarily prioritizes adults, disregarding the fact [...] Read more.
The emotional well-being of a child is crucial for their successful integration into society as a productive individual. While technology has made significant strides in enabling machines to decipher human emotional signals, current research in emotion recognition primarily prioritizes adults, disregarding the fact that children develop emotional awareness at an early stage. This highlights the need to explore how machines can recognize facial expressions in children, although the absence of a standardized database poses a challenge. In this study, we propose a system that employs Convolutional-Neural-Network (CNN)-based models, such as VGG19, VGG16, and Resnet50, as feature extractors, and Support Vector Machine (SVM) and Decision Tree (DT) for classification, to automatically recognize children’s expressions using a video dataset, namely Children’s Spontaneous Facial Expressions (LIRIS-CSE). Our system is evaluated through various experimental setups, including 80–20% split, K-Fold Cross-Validation (K-Fold CV), and leave one out cross-validation (LOOCV), for both image-based and video-based classification. Remarkably, our research achieves a promising classification accuracy of 99% for image-based classification, utilizing features from all three networks with SVM using 80–20% split and K-Fold CV. For video-based classification, we achieve 94% accuracy using features from VGG19 with SVM using LOOCV. These results surpass the performance of the original work, which reported an average image-based classification accuracy of 75% on their LIRIS-CSE dataset. The favorable outcomes obtained from our research can pave the way for the practical application of our proposed emotion recognition methodology in real-world scenarios. Full article
(This article belongs to the Special Issue Computer Vision Imaging Technology and Application)
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13 pages, 47182 KiB  
Article
iDehaze: Supervised Underwater Image Enhancement and Dehazing via Physically Accurate Photorealistic Simulations
by Mehdi Mousavi, Rolando Estrada and Ashwin Ashok
Electronics 2023, 12(11), 2352; https://doi.org/10.3390/electronics12112352 - 23 May 2023
Cited by 1 | Viewed by 1263
Abstract
Underwater image enhancement and turbidity removal (dehazing) is a very challenging problem, not only due to the sheer variety of environments where it is applicable, but also due to the lack of high-resolution, labelled image data. In this paper, we present a novel, [...] Read more.
Underwater image enhancement and turbidity removal (dehazing) is a very challenging problem, not only due to the sheer variety of environments where it is applicable, but also due to the lack of high-resolution, labelled image data. In this paper, we present a novel, two-step deep learning approach for underwater image dehazing and colour correction. In iDehaze, we leverage computer graphics to physically model light propagation in underwater conditions. Specifically, we construct a three-dimensional, photorealistic simulation of underwater environments, and use them to gather a large supervised training dataset. We then train a deep convolutional neural network to remove the haze in these images, then train a second network to transform the colour space of the dehazed images onto a target domain. Experiments demonstrate that our two-step iDehaze method is substantially more effective at producing high-quality underwater images, achieving state-of-the-art performance on multiple datasets. Code, data and benchmarks will be open sourced. Full article
(This article belongs to the Special Issue Computer Vision Imaging Technology and Application)
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18 pages, 4627 KiB  
Article
Detecting Plant Disease in Corn Leaf Using EfficientNet Architecture—An Analytical Approach
by Fathimathul Rajeena P. P., Aswathy S. U., Mohamed A. Moustafa and Mona A. S. Ali
Electronics 2023, 12(8), 1938; https://doi.org/10.3390/electronics12081938 - 20 Apr 2023
Cited by 9 | Viewed by 2597
Abstract
The various corn diseases that affect agriculture go unnoticed by farmers. Each day, more crops fail due to diseases as there is no effective treatment or a way to identify the illness. Common rust, blight, and the northern leaf grey spot are the [...] Read more.
The various corn diseases that affect agriculture go unnoticed by farmers. Each day, more crops fail due to diseases as there is no effective treatment or a way to identify the illness. Common rust, blight, and the northern leaf grey spot are the most prevalent corn diseases. The presence of a disease cannot be accurately detected by simply looking at the plant. This will lead to improper pesticide use, which harms people by bringing on chronic diseases. Therefore, maintaining food security depends on accurate and automatic disease detection. It might be possible to save time and stop crop degradation before it takes place by utilising digital technologies. Hence, applying modern digital technologies to identify the disease in the damaged corn fields automatically will be more advantageous to the farmers. Many academics have recently become interested in deep learning, which has aided in creating an exact and autonomous picture classification scheme. The use of deep learning techniques and their adjustments for detecting corn illnesses can greatly assist contemporary agriculture. To find plant leaf diseases, we employ image acquisition, preprocessing, and classification processes. Preprocessing includes procedures such as reading images, resizing images, and data augmentation. The suggested project is based on EfficientNet and improves the precision of the database of corn leaf diseases by tweaking the variables. Tests are run using DenseNet and Resnet on the test dataset to confirm the precision and robustness of this approach. The recognition accuracy of 98.85% that can be achieved using this method, according to experimental results, is significantly higher than those of other cutting-edge techniques. Full article
(This article belongs to the Special Issue Computer Vision Imaging Technology and Application)
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