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Image Processing and Computer Vision Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 July 2025) | Viewed by 1971

Special Issue Editors


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Guest Editor
Computing and Numerical Analysis Department, University of Córdoba, 14014 Cordoba, Spain
Interests: virtual reality; augmented reality; artificial intelligence; computer vision; computer graphics; soft computing; technology for society and inclusion
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Economics, Engineering, Society and Business Organization (DEIM), University of Tuscia, 01100 Viterbo, Italy
Interests: artificial intelligence; machine learning; neural networks; photogrammetry; digital elevation models; blockchain; anomaly detection; computer vision; healthcare; education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image processing and computer vision are foundational technologies that are driving advancements in diverse domains, such as healthcare, transportation, entertainment, and immersive experiences powered by augmented and virtual reality (AR/VR). These fields, which are increasingly integrated with artificial intelligence (AI) and machine learning (ML), enable the development of automated systems that analyze, interpret, and act upon visual data. Advancements in computer vision enhance the accuracy of image recognition, real-time object tracking, and immersive AR/VR applications, offering new opportunities in fields such as training simulation, accessibility, and entertainment.

The synergy between image processing, computer vision, and AI has fostered the development of intelligent systems that are capable of solving complex visual challenges. Additionally, these technologies are increasingly being employed to promote inclusion, accessibility, and social good, in order to address societal challenges. This Special Issue aims to highlight innovative research and comprehensive reviews on the latest innovations in image processing and computer vision applications, including their integration with AR/VR technologies.

The scope of this Special Issue includes, but is not limited to,  the following topics:

  • Medical imaging and diagnostics.
  • Autonomous vehicles and traffic monitoring.
  • Augmented and virtual reality applications.
  • Object detection, tracking, and scene understanding.
  • Surveillance and security systems.
  • Generative models and image synthesis.
  • AI-driven image segmentation and classification.
  • Integration of vision systems with robotics.
  • Efficient algorithms for real-time image processing.
  • Vision systems for accessibility.
  • Multilingual and inclusive image captioning and description systems.
  • Applications in art, cultural heritage preservation, and virtual museums.

Dr. Enrique Yeguas-Bolivar
Dr. Andrea Zingoni
Dr. Juri Taborri
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. 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

  • image processing
  • computer vision
  • artificial intelligence
  • machine learning
  • real-time systems
  • AR/VR applications
  • object detection
  • image segmentation
  • inclusive technologies
  • visual data analysis

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Published Papers (3 papers)

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Research

19 pages, 3382 KiB  
Article
LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
by Rafael Muñoz-Salinas, Jianheng Liu, Francisco J. Romero-Ramirez, Manuel J. Marín-Jiménez and Fu Zhang
Appl. Sci. 2025, 15(15), 8743; https://doi.org/10.3390/app15158743 - 7 Aug 2025
Viewed by 312
Abstract
This paper presents a robust framework for monocular camera pose estimation by leveraging high-fidelity, pre-built 3D LiDAR maps. The core of our approach is a render-and-match pipeline that synthesizes photorealistic views from a dense LiDAR point cloud. By detecting and matching keypoints between [...] Read more.
This paper presents a robust framework for monocular camera pose estimation by leveraging high-fidelity, pre-built 3D LiDAR maps. The core of our approach is a render-and-match pipeline that synthesizes photorealistic views from a dense LiDAR point cloud. By detecting and matching keypoints between these synthetic images and the live camera feed, we establish reliable 3D–2D correspondences for accurate pose estimation. We evaluate two distinct strategies: an Online Rendering and Tracking method that renders views on the fly, and an Offline Keypoint-Map Tracking method that precomputes a keypoint map for known trajectories, optimizing for computational efficiency. Comprehensive experiments demonstrate that our framework significantly outperforms several state-of-the-art visual SLAM systems in both accuracy and tracking consistency. By anchoring localization to the stable geometric information from the LiDAR map, our method overcomes the reliance on photometric consistency that often causes failures in purely image-based systems, proving particularly effective in challenging real-world environments. Full article
(This article belongs to the Special Issue Image Processing and Computer Vision Applications)
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21 pages, 2346 KiB  
Article
Explainable Liver Segmentation and Volume Assessment Using Parallel Cropping
by Nitin Satpute, Nikhil B. Gaikwad, Smith K. Khare, Juan Gómez-Luna and Joaquín Olivares
Appl. Sci. 2025, 15(14), 7807; https://doi.org/10.3390/app15147807 - 11 Jul 2025
Viewed by 447
Abstract
Accurate liver segmentation and volume estimation from CT images are critical for diagnosis, surgical planning, and treatment monitoring. This paper proposes a GPU-accelerated voxel-level cropping method that localizes the liver region in a single pass, significantly reducing unnecessary computation and memory transfers. We [...] Read more.
Accurate liver segmentation and volume estimation from CT images are critical for diagnosis, surgical planning, and treatment monitoring. This paper proposes a GPU-accelerated voxel-level cropping method that localizes the liver region in a single pass, significantly reducing unnecessary computation and memory transfers. We integrate this pre-processing step into two segmentation pipelines: a traditional Chan-Vese model and a deep learning U-Net trained on the LiTS dataset. After segmentation, a seeded region growing algorithm is used for 3D liver volume assessment. Our method reduces unnecessary image data by an average of 90%, speeds up segmentation by 1.39× for Chan-Vese, and improves dice scores from 0.938 to 0.960. When integrated into U-Net pipelines, the post-processed dice score rises drastically from 0.521 to 0.956. Additionally, the voxel-based cropping approach achieves a 2.29× acceleration compared to state-of-the-art slice-based methods in 3D volume assessment. Our results demonstrate high segmentation accuracy and precise volume estimates with errors below 2.5%. This proposal offers a scalable, interpretable, efficient liver segmentation and volume assessment solution. It eliminates unwanted artifacts and facilitates real-time deployment in clinical environments where transparency and resource constraints are critical. It is also tested in other anatomical structures such as skin, lungs, and vessels, enabling broader applicability in medical imaging. Full article
(This article belongs to the Special Issue Image Processing and Computer Vision Applications)
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16 pages, 7072 KiB  
Article
Automatic Identification and Description of Jewelry Through Computer Vision and Neural Networks for Translators and Interpreters
by José Manuel Alcalde-Llergo, Aurora Ruiz-Mezcua, Rocío Ávila-Ramírez, Andrea Zingoni, Juri Taborri and Enrique Yeguas-Bolívar
Appl. Sci. 2025, 15(10), 5538; https://doi.org/10.3390/app15105538 - 15 May 2025
Viewed by 749
Abstract
Identifying jewelry pieces presents a significant challenge due to the wide range of styles and designs. Currently, precise descriptions are typically limited to industry experts. However, translators and interpreters often require a comprehensive understanding of these items. In this study, we introduce an [...] Read more.
Identifying jewelry pieces presents a significant challenge due to the wide range of styles and designs. Currently, precise descriptions are typically limited to industry experts. However, translators and interpreters often require a comprehensive understanding of these items. In this study, we introduce an innovative approach to automatically identify and describe jewelry using neural networks. This method enables translators and interpreters to quickly access accurate information, aiding in resolving queries and gaining essential knowledge about jewelry. Our model operates at three distinct levels of description, employing computer vision techniques and image captioning to emulate expert analysis of accessories. The key innovation involves generating natural language descriptions of jewelry across three hierarchical levels, capturing nuanced details of each piece. Different image captioning architectures are utilized to detect jewels in images and generate descriptions with varying levels of detail. To demonstrate the effectiveness of our approach in recognizing diverse types of jewelry, we assembled a comprehensive database of accessory images. The evaluation process involved comparing various image captioning architectures, focusing particularly on the encoder–decoder model, crucial for generating descriptive captions. After thorough evaluation, our final model achieved a captioning accuracy exceeding 90%. Full article
(This article belongs to the Special Issue Image Processing and Computer Vision Applications)
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