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Applied Sciences
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Latest Research on Computer Vision and Image Processing
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Latest Research on 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: 25 May 2025 | Viewed by 4101

Special Issue Editors

Dr. Manuel Jesús Rodríguez Valido

E-Mail Website
Guest Editor
Department of Industrial Engineering, Universidad de La Laguna, 38203 San Cristóbal de La Laguna, Spain
Interests: smart sensor networks; FPGA image processing; Internet of Things; autonomous driving; sustainable electric mobility
Special Issues, Collections and Topics in MDPI journals
Dr. Fernando Perez Nava

E-Mail Website
Guest Editor
Department of Computer Engineering and Systems, Universidad de La Laguna, 38203 San Cristóbal de La Laguna, Spain
Interests: image and video processing; computer vision; virtual reality
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gustavo Sutter Capristo

E-Mail Website
Guest Editor
Departamento de Tecnología Electrónica y de las Comunicaciones, Universidad Autónoma de Madrid, 28049 Madrid, Spain
Interests: high-performance computing (HPC)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer vision is an artificial intelligence discipline focused on instructing computers to comprehend and interpret visual data from the surrounding environment. By harnessing digital images captured by cameras and videos, in conjunction with deep learning algorithms, computers can proficiently discern and categorize objects, subsequently enabling them to respond to visual stimuli effectively.

Currently, this discipline is supported by the concepts of image processing, feature detection and matching by pattern recognition and driven by artificial intelligence technologies such as machine learning and deep learning, neural networks, image recognition and classification and object detection.

Challenges and limitations in computer vision are related to the following: data quality and quantity to increase accuracy; environmental sustainability, trade-off between increased computational requirements and energy efficiency; ethical and privacy concerns. 

Research topics and application fields of interest for this Special Issue include, but are not limited to, the following:

  • Augmented reality
    • Transformatives sectors: manufacturing, retail, education and technological advances.
  • Vision models
    • Deep learning techniques in computer vision.
    • Integration of vision and language in robotics.
  • Advanced satellite vision
    • Monitoring environmental and urban changes.
    • Disaster response and management.
    • Agricultural applications.
    • Climate change analysis.
  • 3D computer vision
    • Novel view rendering.
    • Generation of synthetic data for deep learning.
    • Enhancing autonomous vehicles and digital twin modeling.
  • Ethics in computer vision
    • Related to bias and privacy.
  • Edge computing
  • Computer vision in healthcare
    • Medical image analysis.
    • Aiding surgeries.
    • Patient monitoring.
  • Synthetic data and generative AI
  • Real-time computer vision
    • Applications in enhancing security.
    • Crowd monitoring and management.
    • Industrial safety.
  • Deepfake detection

Dr. Manuel Jesús Rodríguez Valido
Dr. Fernando Perez Nava
Prof. Dr. Gustavo Sutter Capristo
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

  • computer vision
  • image and video processing
  • image and video understanding
  • computer vision applications
  • machine learning
  • artificial intelligence
  • ethics
  • deepfakes
  • sustainability

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

29 pages, 6885 KiB  
Article
Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
by Younes Karmoude, Soufiane Idbraim, Souad Saidi, Antoine Masse and Manuel Arbelo
Appl. Sci. 2025, 15(6), 3231; https://doi.org/10.3390/app15063231 - 16 Mar 2025
Viewed by 901
Abstract
The argan tree (Argania spinosa) is a rare species native to southwestern Morocco, valued for its fruit, which produces argan oil, a highly prized natural product with nutritional, health, and cosmetic benefits. However, increasing deforestation poses a significant threat to its [...] Read more.
The argan tree (Argania spinosa) is a rare species native to southwestern Morocco, valued for its fruit, which produces argan oil, a highly prized natural product with nutritional, health, and cosmetic benefits. However, increasing deforestation poses a significant threat to its survival. This study monitors changes in an argan forest near Agadir, Morocco, from 2017 to 2023 using Sentinel-2 satellite imagery and advanced image processing algorithms. Various machine learning models were evaluated for argan tree detection, with LightGBM achieving the highest accuracy when trained on a dataset integrating spectral bands, temporal features, and vegetation indices information. The model achieved 100% accuracy on tabular test data and 85% on image-based test data. The generated deforestation maps estimated an approximate forest loss of 2.86% over six years. This study explores methods to enhance detection accuracy, provides valuable statistical data for deforestation mitigation, and highlights the critical role of remote sensing, advanced image processing, and artificial intelligence in environmental monitoring and conservation, particularly in argan forests. Full article
(This article belongs to the Special Issue Latest Research on Computer Vision and Image Processing)
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29 pages, 3207 KiB  
Article
Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
by Ivana Žeger, Ivan Šetka, Domagoj Marić and Sonja Grgic
Appl. Sci. 2024, 14(23), 11401; https://doi.org/10.3390/app142311401 - 7 Dec 2024
Viewed by 861
Abstract
Decolorization is an image processing technique that converts a color input image into a grayscale image. This paper discusses the decolorization process and provides an overview of the methods based on the different principles used: basic conversion from RGB to YUV format using [...] Read more.
Decolorization is an image processing technique that converts a color input image into a grayscale image. This paper discusses the decolorization process and provides an overview of the methods based on the different principles used: basic conversion from RGB to YUV format using ITU Recommendations 601, 709, and 2020; basic conversion from RGB to LAB color space; the method using cumulative distribution function of color channels; one global decolorization method; and one based on deep learning. The grayscale images produced by these methods were evaluated using four objective metrics, allowing for a thorough analysis and comparison of the decolorization results. Additionally, the execution speed of the algorithms was assessed, providing insight into their performance efficiency. The results demonstrate that different metrics evaluate the decolorization methods differently, highlighting the importance of selecting an appropriate metric that aligns with the subsequent image processing tasks following decolorization. Furthermore, it was shown that the decolorization methods depend on the content of the images, performing better on natural images than on artificially generated ones. The decolorization methods were also examined in the context of object segmentation and edge detection. The results from segmentation and edge detection were aligned with the decolorization results, revealing that certain objective metrics for evaluating decolorization more effectively assessed the properties of the decolorized images, which are crucial for successful object segmentation and edge detection. Full article
(This article belongs to the Special Issue Latest Research on Computer Vision and Image Processing)
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21 pages, 2276 KiB  
Article
Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
by Nicholas M. DiFilippo, Musa K. Jouaneh and Alexander D. Jedson
Appl. Sci. 2024, 14(14), 6301; https://doi.org/10.3390/app14146301 - 19 Jul 2024
Cited by 1 | Viewed by 1169
Abstract
This paper investigates varying the operating conditions of a neural network in a robotic system using a low-cost webcam to achieve optimal settings in order to detect crossed-recess screws on laptops, a necessary step in the realization of automated disassembly systems. A study [...] Read more.
This paper investigates varying the operating conditions of a neural network in a robotic system using a low-cost webcam to achieve optimal settings in order to detect crossed-recess screws on laptops, a necessary step in the realization of automated disassembly systems. A study was performed that varied the lighting conditions, velocity, and number of passes the robot made over the laptop, as well as the network size of a YOLO-v5 neural network. The analysis reveals that specific combinations of operating parameters and neural network configurations can significantly improve detection accuracy. Specifically, the best results for the majority of laptops were obtained when the system ran at medium velocity (10 and 15 mm/s), with a light, and the neural network was run with an extra large network. Additionally, the results show that screw characteristics like the screw hole depth, the presence of a taper in the screw hole, screw hole location, and the color difference between the laptop cover and the screw color impact the system’s overall detection rate, with the most important factor being the depth of the screw. Full article
(This article belongs to the Special Issue Latest Research on Computer Vision and Image Processing)
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