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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 May 2026 | Viewed by 9786

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Dr. Pedro Couto

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Guest Editor

CITAB—Centre for the Research and Technology of Agro-Environmental and Biological Sciences, UTAD—University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
Interests: computer vision; image processing; medical image processing; artificial intelligence
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Special Issue Information

Dear Colleagues,

Grounded on visual data, computer vision aims to enable computers to see, understand, decide, and act. Over the years, computational vision has rapidly gained popularity in a wide range of areas, including industry, transportation, agriculture, and medicine. Nowadays, artificial intelligence-powered vision systems are driving the sector to previously unseen levels of popularity by increasing their efficiency and accuracy.

Thus, the applications of such systems are expected to continue to increase alongside the artificial intelligence, machine learning, and deep learning algorithms that are being developed within these frameworks, which have recently achieved great success over conventional techniques. Hence, the community has high expectations for where they foresee these new artificial intelligence-powered techniques in the coming years, in terms of discipline and practice. Thus, this Special Issue aims to provie insight on this future.

This Special Issue aims at presenting new technical approaches in computer vision research and development with particular emphasis on the engineering and technological aspects of image processing and computer vision and their contributions to a wide range of application fields, including (but not limited to) the following:

Agriculture;
Healthcare;
Environmental monitoring;
Security and surveillance;
Automotive industry;
Entertainment;
Robotics.

Dr. Pedro Couto
Guest Editor

Manuscript Submission Information

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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

computer vision
image processing
video processing
artificial intelligence
and machine learning

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Published Papers (5 papers)

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Research

Jump to: Review, Other

12 pages, 2384 KB

Open AccessArticle

Image Processing Technology Applied to Fluorescent Rapid Tests for Influenza A and B Viruses

by Yu-Lin Wu, Wei-Chien Weng, Wen-Fung Pan and Yu-Cheng Lin

Appl. Sci. 2025, 15(21), 11523; https://doi.org/10.3390/app152111523 - 28 Oct 2025

Viewed by 447

Abstract

This study establishes a detection method based on image recognition to interpret and quantitatively analyze fluorescent rapid test kits for influenza. The method operates in a dark chamber equipped with a UV-LED, where the fluorescence of the test kit is excited by the UV-LED and subsequently captured using a camera module. The captured images are processed by segmenting the regions of interest (ROI), converting them to grayscale images, and analyzing the grayscale value distributions to identify the control (C) and test (T) line regions. By comparing the values of the C and T lines, the concentration is determined to achieve quantitative analysis. In the linearity validation experiments, the concentrations of influenza A (H1N1) specimens are 2, 4, 6, 8, and 10 ng/mL, achieving a coefficient of determination (R²) of 0.9923. For influenza B (Yamagata) specimens, concentrations of 6, 8, 10, 12.5, and 25 ng/mL resulted in an R² of 0.9878. The established method enables the detection of both influenza A (H1N1) and influenza B (Yamagata), replacing visual qualitative interpretation with quantitative analysis. Currently, the detection method developed in this paper is designed for use exclusively in a dark chamber and is specifically applied to fluorescent rapid tests. It cannot be directly used with conventional colloidal gold-based rapid test reagents. In the future, the proposed detection approach could be integrated with neural networks to enable its application to non-fluorescent rapid test interpretation and to operate beyond the dark chamber environment, for example by utilizing smartphone imaging for result interpretation under normal lighting conditions. Full article

(This article belongs to the Special Issue Recent Advances and New Trends in Computer Vision and Image Processing)

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22 pages, 17900 KB

Open AccessArticle

Custom Material Scanning System for PBR Texture Acquisition: Hardware Design and Digitisation Workflow

by Lunan Wu, Federico Morosi and Giandomenico Caruso

Appl. Sci. 2025, 15(20), 10911; https://doi.org/10.3390/app152010911 - 11 Oct 2025

Viewed by 1152

Abstract

Real-time rendering is increasingly used in augmented and virtual reality (AR/VR), interactive design, and product visualisation, where materials must prioritise efficiency and consistency rather than the extreme accuracy required in offline rendering. In parallel, the growing demand for personalised and customised products has created a need for digital materials that can be generated in-house without relying on expensive commercial systems. To address these requirements, this paper presents a low-cost digitisation workflow based on photometric stereo. The system integrates a custom-built scanner with cross-polarised illumination, automated multi-light image acquisition, a dual-stage colour calibration process, and a node-based reconstruction pipeline that produces albedo and normal maps. A reproducible evaluation methodology is also introduced, combining perceptual colour-difference analysis using the CIEDE2000 (

Δ E_{00}

) metric with angular-error assessment of normal maps on known-geometry samples. By openly providing the workflow, bill of materials, and implementation details, this work delivers a practical and replicable solution for reliable material capture in real-time rendering and product customisation scenarios. Full article

(This article belongs to the Special Issue Recent Advances and New Trends in Computer Vision and Image Processing)

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23 pages, 2351 KB

Open AccessArticle

Ensemble of Efficient Vision Transformers for Insect Classification

by Marius Alexandru Dinca, Dan Popescu, Loretta Ichim and Nicoleta Angelescu

Appl. Sci. 2025, 15(13), 7610; https://doi.org/10.3390/app15137610 - 7 Jul 2025

Cited by 2 | Viewed by 1607

Abstract

Real-time identification of insect pests is an important research direction in modern agricultural management, directly influencing crop health and yield. Recent advances in computer vision and deep learning, especially vision transformer (ViT) architectures, have demonstrated great potential in addressing this challenge. The present study explores the possibility of combining some ViT models for the insect pest classification task to improve system performance and robustness. Two popular and widely known datasets, D0 and IP102, which consist of diverse digital images with complex contexts of insect pests, were used. The proposed methodology involved training several individual ViT models on the chosen datasets, finally creating an ensemble strategy to fuse their results. A new combination method was used, based on the F1 score of individual models and a meta-classifier structure, capitalizing on the strengths of each base model and effectively capturing complex features for the final prediction. The experimental results indicated that the proposed ensemble methodology significantly outperformed the individual ViT models, observing notable improvements in classification accuracy for both datasets. Specifically, the ensemble model achieved a test accuracy of 99.87% and an F1 score of 99.82% for the D0 dataset, and an F1 score of 84.25% for IP102, demonstrating the method’s effectiveness for insect pest classification from different datasets. The noted features pave the way for implementing reliable and effective solutions in the agricultural pest management process. Full article

(This article belongs to the Special Issue Recent Advances and New Trends in Computer Vision and Image Processing)

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Review

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17 pages, 43598 KB

Open AccessReview

Body Measurements for Digital Forensic Comparison of Individuals—An Overview of Current Developments

by Sabine Richter and Dirk Labudde

Appl. Sci. 2025, 15(23), 12518; https://doi.org/10.3390/app152312518 - 25 Nov 2025

Viewed by 385

Abstract

Forensic identification of individuals faces significant challenges, particularly when conventional biometric features such as the face are hidden. This paper examines the historical development and revival of body patterns (anthropometric rig) as biometric comparison feature, from historical Bertillonage to modern, computer-assisted methods such as digital anthropometric rig matching and the connection to 3D human pose estimation (HPE). It highlights both the mathematical and methodological foundations of this revival, as well as the potential and limitations of applying artificial intelligence (AI) in the context of digital anthropometric rig matching. The aim is to highlight the development of potential and challenges for the forensic validity of the person-specific digital skeleton. This clearly shows the time required for manual work, which underlines the need for automation. The time required can be reduced by approaches that use AI. However, these methods are often not yet up to the requirements in a forensic context. Full article

(This article belongs to the Special Issue Recent Advances and New Trends in Computer Vision and Image Processing)

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Other

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41 pages, 1927 KB

Open AccessSystematic Review

Advancements in Small-Object Detection (2023–2025): Approaches, Datasets, Benchmarks, Applications, and Practical Guidance

by Ali Aldubaikhi and Sarosh Patel

Appl. Sci. 2025, 15(22), 11882; https://doi.org/10.3390/app152211882 - 7 Nov 2025

Viewed by 5557

Abstract

Small-object detection (SOD) remains an important and growing challenge in computer vision and is the backbone of many applications, including autonomous vehicles, aerial surveillance, medical imaging, and industrial quality control. Small objects, in pixels, lose discriminative features during deep neural network processing, making them difficult to disentangle from background noise and other artifacts. This survey presents a comprehensive and systematic review of the SOD advancements between 2023 and 2025, a period marked by the maturation of transformer-based architectures and a return to efficient, realistic deployment. We applied the PRISMA methodology for this work, yielding 112 seminal works in the field to ensure the robustness of our foundation for this study. We present a critical taxonomy of the developments since 2023, arranged in five categories: (1) multiscale feature learning; (2) transformer-based architectures; (3) context-aware methods; (4) data augmentation enhancements; and (5) advancements to mainstream detectors (e.g., YOLO). Third, we describe and analyze the evolving SOD-centered datasets and benchmarks and establish the importance of evaluating models fairly. Fourth, we contribute a comparative assessment of state-of-the-art models, evaluating not only accuracy (e.g., the average precision for small objects (AP_S)) but also important efficiency (FPS, latency, parameters, GFLOPS) metrics across standardized hardware platforms, including edge devices. We further use data-driven case studies in the remote sensing, manufacturing, and healthcare domains to create a bridge between academic benchmarks and real-world performance. Finally, we summarize practical guidance for practitioners, the model selection decision matrix, scenario-based playbooks, and the deployment checklist. The goal of this work is to help synthesize the recent progress, identify the primary limitations in SOD, and open research directions, including the potential future role of generative AI and foundational models, to address the long-standing data and feature representation challenges that have limited SOD. Full article

(This article belongs to the Special Issue Recent Advances and New Trends in Computer Vision and Image Processing)

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