Advances in Image Processing and Computer Vision Based on Machine Learning

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4207

Special Issue Editor

Assistant Professor, Department of Electrical, Electronics and Computer Engineering (DIEEI), University of Catania, 95125 Catania, Italy
Interests: audio signal processing; biometrics; IoT; drone/UAV communications; rainfall estimation and monitoring; post-earthquake geolocation; image processing; computer vision; machine learning-based applications
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Special Issue Information

Dear Colleagues,

This Special Issue is devoted to the recent advances in image processing and computer vision. Much of this recent explosion of developments and application areas is due to the powerful capabilities of machine learning algorithms and, more specifically, convolutional neural networks (CNNs).

Computer Vision plays an important role in health care (e.g., COVID-19), anti-crime, and countering hydrogeological disruption. This Special Issue aims to present original, unpublished, and breakthrough research in the metaverse and computer vision focusing on new algorithms and mechanisms, such as artificial intelligence, machine learning, and explainable artificial intelligence (XAI). We aim to bring leading scientists and researchers together and create an interdisciplinary platform for the exchange of computational theories, methodologies, and techniques.

The purpose of this Special Issue is to disseminate research papers or state-of-the-art surveys that pertain to novel or emerging applications in the field of image processing and computer vision based on machine learning algorithms. Papers may contribute to technologies and application areas that have emerged during the past decade. Submissions are particularly welcome in, though not limited to, the areas in the list of keywords below.

Technical Program Committee Member:

Ms. Roberta Avanzato   
E-mail: roberta.avanzato@phd.unict.it
Homepage: https://www.researchgate.net/profile/Roberta-Avanzato
Affiliation: Department of Electrical, Electronics and Computer Engineering (DIEEI), University of Catania, 95124 Catania, Italy
Research Interests: rainfall estimation; geolocation; natural disasters; Internet of Things; UAV; computer networking; biomedical signal processing

Dr. Francesco Beritelli
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 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
  • image segmentation
  • computer vision
  • deep learning
  • machine learning
  • reinforcement learning
  • classification
  • healthcare applications
  • novel industrial applications
  • high-speed computer vision
  • novel applications for 3D vision
  • object recognition
  • object detection
  • object tracking

Published Papers (4 papers)

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Research

18 pages, 7562 KiB  
Article
Graph- and Machine-Learning-Based Texture Classification
by Musrrat Ali, Sanoj Kumar, Rahul Pal, Manoj K. Singh and Deepika Saini
Electronics 2023, 12(22), 4626; https://doi.org/10.3390/electronics12224626 - 12 Nov 2023
Viewed by 944
Abstract
The analysis of textures is an important task in image processing and computer vision because it provides significant data for image retrieval, synthesis, segmentation, and classification. Automatic texture recognition is difficult, however, and necessitates advanced computational techniques due to the complexity and diversity [...] Read more.
The analysis of textures is an important task in image processing and computer vision because it provides significant data for image retrieval, synthesis, segmentation, and classification. Automatic texture recognition is difficult, however, and necessitates advanced computational techniques due to the complexity and diversity of natural textures. This paper presents a method for classifying textures using graphs; specifically, natural and horizontal visibility graphs. The related image natural visibility graph (INVG) and image horizontal visibility graph (IHVG) are used to obtain features for classifying textures. These features are the clustering coefficient and the degree distribution. The suggested outcomes show that the aforementioned technique outperforms traditional ones and even comes close to matching the performance of convolutional neural networks (CNNs). Classifiers such as the support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) are utilized for the categorization. The suggested method is tested on well-known image datasets like the Brodatz texture and the Salzburg texture image (STex) datasets. The results are positive, showing the potential of graph methods for texture classification. Full article
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10 pages, 1247 KiB  
Article
Multi-Modality Tensor Fusion Based Human Fatigue Detection
by Jongwoo Ha, Joonhyuck Ryu and Joonghoon Ko
Electronics 2023, 12(15), 3344; https://doi.org/10.3390/electronics12153344 - 04 Aug 2023
Viewed by 674
Abstract
Multimodal learning is an expanding research area and aims to pursue a better understanding of given data by regarding different modals. Multimodal approaches for qualitative data are used for the quantitative proofing of ground-truth datasets and discovering unexpected phenomena. In this paper, we [...] Read more.
Multimodal learning is an expanding research area and aims to pursue a better understanding of given data by regarding different modals. Multimodal approaches for qualitative data are used for the quantitative proofing of ground-truth datasets and discovering unexpected phenomena. In this paper, we investigate the effect of multimodal learning schemes of quantitative data to assess its qualitative state. We try to interpret human fatigue levels through analyzing video, thermal image and voice data together. The experiment showed that the multimodal approach using three types of data was more effective than the method of using each dataset individually. As a result, we identified the possibility of predicting human fatigue states. Full article
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18 pages, 6534 KiB  
Article
Underwater Image Color Constancy Calculation with Optimized Deep Extreme Learning Machine Based on Improved Arithmetic Optimization Algorithm
by Junyi Yang, Qichao Yu, Sheng Chen and Donghe Yang
Electronics 2023, 12(14), 3174; https://doi.org/10.3390/electronics12143174 - 21 Jul 2023
Cited by 1 | Viewed by 698
Abstract
To overcome the challenges posed by the underwater environment and restore the true colors of marine objects’ surfaces, a novel underwater image illumination estimation model, termed the iterative chaotic improved arithmetic optimization algorithm for deep extreme learning machines (IAOA-DELM), is proposed. In this [...] Read more.
To overcome the challenges posed by the underwater environment and restore the true colors of marine objects’ surfaces, a novel underwater image illumination estimation model, termed the iterative chaotic improved arithmetic optimization algorithm for deep extreme learning machines (IAOA-DELM), is proposed. In this study, the gray edge framework is utilized to extract color features from underwater images, which are employed as input vectors. To address the issue of unstable prediction results caused by the random selection of parameters in DELM, the arithmetic optimization algorithm (AOA) is integrated, and the search segment mapping method is optimized by using hidden layer biases and input layer weights. Furthermore, an iterative chaotic mapping initialization strategy is incorporated to provide AOA with a better initial search proxy. The IAOA-DELM model computes illumination information based on the input color vectors. Experimental evaluations conducted on actual underwater images demonstrate that the proposed IAOA-DELM illumination correction model achieves an accuracy of 96.07%. When compared to the ORELM, ELM, RVFL, and BP models, the IAOA-DELM model exhibits improvements of 6.96%, 7.54%, 8.00%, and 8.89%, respectively, making it the most effective among the compared illumination correction models. Full article
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25 pages, 9839 KiB  
Article
An Improved Median Filter Based on YOLOv5 Applied to Electrochemiluminescence Image Denoising
by Jun Yang, Junyang Chen, Jun Li, Shijie Dai and Yihui He
Electronics 2023, 12(7), 1544; https://doi.org/10.3390/electronics12071544 - 24 Mar 2023
Cited by 1 | Viewed by 1320
Abstract
In many experiments, the electrochemiluminescence images captured by smartphones often have a lot of noise, which makes it difficult for researchers to accurately analyze the light spot information from the captured images. Therefore, it is very important to remove the noise in the [...] Read more.
In many experiments, the electrochemiluminescence images captured by smartphones often have a lot of noise, which makes it difficult for researchers to accurately analyze the light spot information from the captured images. Therefore, it is very important to remove the noise in the image. In this paper, a Center-Adaptive Median Filter (CAMF) based on YOLOv5 is proposed. Unlike other traditional filtering algorithms, CAMF can adjust its size in real-time according to the current pixel position, the center and the boundary frame of each light spot, and the distance between them. This gives CAMF both a strong noise reduction ability and light spot detail protection ability. In our experiment, the evaluation scores of CAMF for the three indicators Peak Signal-to-Noise Ratio (PSNR), Image Enhancement Factor (IEF), and Structural Similarity (SSIM) were 40.47 dB, 613.28 and 0.939, respectively. The results show that CAMF is superior to other filtering algorithms in noise reduction and light spot protection. Full article
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