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Algorithms
Special Issues
Machine Learning Models and Algorithms for Image Processing
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Machine Learning Models and Algorithms for Image Processing

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5397

Special Issue Editors

Dr. Marcin Iwanowski

E-Mail Website
Guest Editor
Institute of Control and Industrial Electronics, Warsaw University of Technology, 00-662 Warszawa, Poland
Interests: computer vision; image processing; machine learning; deep learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bogusław Cyganek

E-Mail Website
Guest Editor
Faculty of Computer Science, Electronics and Telecommunication, Department of Electronics, AGH University of Science and Technology, 30-059 Krakow, Poland
Interests: computer vision; machine learning; artificial intelligence; software development in C++ and Python
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit a paper to the Special Issue of Algorithms entitled “Machine Learning Models and Algorithms for Image Processing”. Applying machine learning algorithms to image processing is currently experiencing intense development. We are looking for papers in this field, reporting new and innovative approaches and in-depth surveys. High-quality papers are solicited to address both theoretical and practical issues of vision-related machine learning. Submissions are welcome both for theoretical development problems as well as applications.

Potential topics include, but are not limited to, object detection, semantic and instance segmentation, image description, deep learning models in computer vision, medical imaging, remote sensing, machine vision, robot vision, underwater image analysis and enhancement, sonar image processing, thermal imaging and object detection/classification in near and far thermal images, image fusion, multi-modal data retrieval, video analysis, anomaly detection, tensor processing for multimedia, driver assisting/monitoring systems, video surveillance, image and video processing for building/construction inspection, image and video in industry, and many more.

Dr. Marcin Iwanowski
Prof. Dr. Bogusław Cyganek
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • machine learning
  • deep learning
  • computer vision
  • image processing
  • object detection
  • semantic and instance segmentation
  • image description
  • medical imaging
  • remote sensing
  • machine vision
  • robot vision
  • video surveillance
  • anomaly detection
  • underwater image enhancement
  • sonar imaging
  • thermal imaging
  • spectra fusion
  • multi-modal data retrieval
  • tensor processing
  • imaging in industry

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Published Papers (5 papers)

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Research

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24 pages, 534 KiB  
Article
Anomaly Detection in High-Dimensional Time Series Data with Scaled Bregman Divergence
by Yunge Wang, Lingling Zhang, Tong Si, Graham Bishop and Haijun Gong
Algorithms 2025, 18(2), 62; https://doi.org/10.3390/a18020062 - 24 Jan 2025
Viewed by 397
Abstract
The purpose of anomaly detection is to identify special data points or patterns that significantly deviate from the expected or typical behavior of the majority of the data, and it has a wide range of applications across various domains. Most existing statistical and [...] Read more.
The purpose of anomaly detection is to identify special data points or patterns that significantly deviate from the expected or typical behavior of the majority of the data, and it has a wide range of applications across various domains. Most existing statistical and machine learning-based anomaly detection algorithms face challenges when applied to high-dimensional data. For instance, the unconstrained least-squares importance fitting (uLSIF) method, a state-of-the-art anomaly detection approach, encounters the unboundedness problem under certain conditions. In this study, we propose a scaled Bregman divergence-based anomaly detection algorithm using both least absolute deviation and least-squares loss for parameter learning. This new algorithm effectively addresses the unboundedness problem, making it particularly suitable for high-dimensional data. The proposed technique was evaluated on both synthetic and real-world high-dimensional time series datasets, demonstrating its effectiveness in detecting anomalies. Its performance was also compared to other density ratio estimation-based anomaly detection methods. Full article
(This article belongs to the Special Issue Machine Learning Models and Algorithms for Image Processing)
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14 pages, 1758 KiB  
Article
Unsupervised Temporal Adaptation in Skeleton-Based Human Action Recognition
by Haitao Tian and Pierre Payeur
Algorithms 2024, 17(12), 581; https://doi.org/10.3390/a17120581 - 16 Dec 2024
Viewed by 434
Abstract
With deep learning approaches, the fundamental assumption of data availability can be severely compromised when a model trained on a source domain is transposed to a target application domain where data are unlabeled, making supervised fine-tuning mostly impossible. To overcome this limitation, the [...] Read more.
With deep learning approaches, the fundamental assumption of data availability can be severely compromised when a model trained on a source domain is transposed to a target application domain where data are unlabeled, making supervised fine-tuning mostly impossible. To overcome this limitation, the present work introduces an unsupervised temporal-domain adaptation framework for human action recognition from skeleton-based data that combines Contrastive Prototype Learning (CPL) and Temporal Adaptation Modeling (TAM), with the aim of transferring the knowledge learned from a source domain to an unlabeled target domain. The CPL strategy, inspired by recent success in contrastive learning applied to skeleton data, learns a compact temporal representation from the source domain, from which the TAM strategy leverages the capacity for self-training to adapt the representation to a target application domain using pseudo-labels. The research demonstrates that simultaneously solving CPL and TAM effectively enables the training of a generalizable human action recognition model that is adaptive to both domains and overcomes the requirement of a large volume of labeled skeleton data in the target domain. Experiments are conducted on multiple large-scale human action recognition datasets such as NTU RGB+D, PKU MMD, and Northwestern–UCLA to comprehensively evaluate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Machine Learning Models and Algorithms for Image Processing)
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22 pages, 5018 KiB  
Article
Color Standardization of Chemical Solution Images Using Template-Based Histogram Matching in Deep Learning Regression
by Patrycja Kwiek and Małgorzata Jakubowska
Algorithms 2024, 17(8), 335; https://doi.org/10.3390/a17080335 - 1 Aug 2024
Cited by 1 | Viewed by 1182
Abstract
Color distortion in an image presents a challenge for machine learning classification and regression when the input data consists of pictures. As a result, a new algorithm for color standardization of photos is proposed, forming the foundation for a deep neural network regression [...] Read more.
Color distortion in an image presents a challenge for machine learning classification and regression when the input data consists of pictures. As a result, a new algorithm for color standardization of photos is proposed, forming the foundation for a deep neural network regression model. This approach utilizes a self-designed color template that was developed based on an initial series of studies and digital imaging. Using the equalized histogram of the R, G, B channels of the digital template and its photo, a color mapping strategy was computed. By applying this approach, the histograms were adjusted and the colors of photos taken with a smartphone were standardized. The proposed algorithm was developed for a series of images where the entire surface roughly maintained a uniform color and the differences in color between the photographs of individual objects were minor. This optimized approach was validated in the colorimetric determination procedure of vitamin C. The dataset for the deep neural network in the regression variant was formed from photos of samples under two separate lighting conditions. For the vitamin C concentration range from 0 to 87.72 µg·mL−1, the RMSE for the test set ranged between 0.75 and 1.95 µg·mL−1, in comparison to the non-standardized variant, where this indicator was at the level of 1.48–2.29 µg·mL−1. The consistency of the predicted concentration results with actual data, expressed as R2, ranged between 0.9956 and 0.9999 for each of the standardized variants. This approach allows for the removal of light reflections on the shiny surfaces of solutions, which is a common problem in liquid samples. This color-matching algorithm has universal character, and its scope of application is not limited. Full article
(This article belongs to the Special Issue Machine Learning Models and Algorithms for Image Processing)
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23 pages, 6574 KiB  
Article
Sub-Band Backdoor Attack in Remote Sensing Imagery
by Kazi Aminul Islam, Hongyi Wu, Chunsheng Xin, Rui Ning, Liuwan Zhu and Jiang Li
Algorithms 2024, 17(5), 182; https://doi.org/10.3390/a17050182 - 28 Apr 2024
Cited by 1 | Viewed by 1522
Abstract
Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent [...] Read more.
Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent years and has been widely applied to remote image analysis, achieving state-of-the-art (SOTA) performance. However, AI models are vulnerable and can be easily deceived or poisoned. A malicious user may poison an AI model by creating a stealthy backdoor. A backdoored AI model performs well on clean data but behaves abnormally when a planted trigger appears in the data. Backdoor attacks have been extensively studied in machine learning-based computer vision applications with natural images. However, much less research has been conducted on remote sensing imagery, which typically consists of many more bands in addition to the red, green, and blue bands found in natural images. In this paper, we first extensively studied a popular backdoor attack, BadNets, applied to a remote sensing dataset, where the trigger was planted in all of the bands in the data. Our results showed that SOTA defense mechanisms, including Neural Cleanse, TABOR, Activation Clustering, Fine-Pruning, GangSweep, Strip, DeepInspect, and Pixel Backdoor, had difficulties detecting and mitigating the backdoor attack. We then proposed an explainable AI-guided backdoor attack specifically for remote sensing imagery by placing triggers in the image sub-bands. Our proposed attack model even poses stronger challenges to these SOTA defense mechanisms, and no method was able to defend it. These results send an alarming message about the catastrophic effects the backdoor attacks may have on satellite imagery. Full article
(This article belongs to the Special Issue Machine Learning Models and Algorithms for Image Processing)
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Review

Jump to: Research

14 pages, 2535 KiB  
Review
Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets
by Reza Khoshkbary Rezayiye, Clemente Ibarra-Castanedo and Xavier Maldague
Algorithms 2024, 17(10), 439; https://doi.org/10.3390/a17100439 - 1 Oct 2024
Viewed by 940
Abstract
This study reviews advanced methods for corrosion detection and characterization in pipes using thermography, with a focus on addressing the limitations posed by small datasets. Thermography captures temperature distributions on the surface of pipes to identify subsurface defects. The challenges of sequential data [...] Read more.
This study reviews advanced methods for corrosion detection and characterization in pipes using thermography, with a focus on addressing the limitations posed by small datasets. Thermography captures temperature distributions on the surface of pipes to identify subsurface defects. The challenges of sequential data processing, neural network performance, feature extraction, and dataset size are discussed, with proposed solutions such as advanced algorithms, feature selection techniques, and data augmentation. Given the significant gap in the current literature, there is a need for larger, more diverse datasets to train more robust and accurate machine learning models. A case study combining experimental data with Finite Element Method (FEM) simulations demonstrates that augmenting datasets with synthetic data significantly improves defect detection accuracy. These findings highlight the potential of integrating thermography with machine learning to enhance defect detection, providing insights for future research and practical applications. Full article
(This article belongs to the Special Issue Machine Learning Models and Algorithms for Image Processing)
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