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Recent Advances and New Trends in Computer Vision and Image...
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Recent Advances and New Trends in Computer Vision and Image Processing

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 October 2025 | Viewed by 1550

Special Issue Editor

Dr. Pedro Couto

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

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. 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 (2 papers)

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Research

22 pages, 17900 KB  
Article
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 206
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 [...] Read more.
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 (ΔE00) 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  
Article
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 1 | Viewed by 831
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 [...] Read more.
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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