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Algorithms
Special Issues
Visual Attributes in Computer Vision Applications
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Visual Attributes in Computer Vision Applications

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 2755

Special Issue Editor

Dr. Md Baharul Islam

E-Mail Website
Guest Editor
Department of Computing & Software Engineering, Florida Gulf Coast, Fort Myers, FL 33965, USA
Interests: image processing; computer vision; human-centered computing

Special Issue Information

Dear Colleagues,

Recent advancements in computer vision algorithms have unlocked the potential of visual attributes across a wide spectrum of emerging research areas and innovative real-world applications. These include, but are not limited to, healthcare, autonomous vehicles, activity recognition, facial and gesture analysis, biomedical imaging, vision-based rehabilitation, augmented reality (AR), virtual reality (VR), mixed reality (MR), and other intelligent systems. Both static and dynamic visual attributes are key to shaping these applications. Optimizing the use of individual or combined visual attributes has the potential to significantly enhance the performance, accuracy, and impact of these systems for end users and industry. This Special Issue aims to accelerate progress in this rapidly evolving field by fostering interdisciplinary collaboration and engagement within the visual computing and intelligent computer vision communities.

We welcome submissions on a range of topics, including but not limited to visual quality computing, visual cues for activity and gesture analysis, healthcare applications, image/video segmentation and inpainting, real-world or in-the-wild applications, low-level intelligent vision systems, visual field augmentation, transformation, and gamification.

Dr. Md Baharul Islam
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. 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

  • visual quality enhancement and restoration
  • image and video inpainting and super-resolutions
  • gesture recognition for human–computer interactions
  • sign language recognition systems
  • computer vision for medical imaging
  • visual attributes in diagnostic healthcare systems
  • vision-based rehabilitation and assistive technologies
  • visual cues for remote healthcare monitoring
  • vision for autonomous driving systems
  • object detection and tracking in dynamic environments
  • scene understanding and semantic segmentation for autonomous systems
  • visual data for edge and cloud computing
  • low-level vision for intelligent systems
  • AR/VR/MR applications in training and simulation
  • gamification in vision-based applications
  • immersive environments for learning and training
  • object completion and inpainting in vision systems
  • vision-based surveillance and security systems
  • visual perception under adverse conditions (e.g., low light, weather, etc.)
  • computer vision for environmental monitoring (e.g., agriculture, forestry, etc.)

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

Published Papers (2 papers)

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Research

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16 pages, 12755 KiB  
Article
Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest
by Gabriel R. Pardini, Paulo M. Tasinaffo, Elcio H. Shiguemori, Tahisa N. Kuck, Marcos R. O. A. Maximo and William R. Gyotoku
Algorithms 2025, 18(2), 102; https://doi.org/10.3390/a18020102 - 13 Feb 2025
Viewed by 756
Abstract
The Amazon biome is frequently targeted by illegal activities, with clandestine mining being one of the most prominent. Due to the dense forest cover, criminals often rely on covert aviation as a logistical tool to supply remote locations and sustain these activities. This [...] Read more.
The Amazon biome is frequently targeted by illegal activities, with clandestine mining being one of the most prominent. Due to the dense forest cover, criminals often rely on covert aviation as a logistical tool to supply remote locations and sustain these activities. This work presents an enhancement to a previously developed landing strip detection algorithm tailored for the Amazon biome. The initial algorithm utilized satellite images combined with the use of Convolutional Neural Networks (CNNs) to find the targets’ spatial locations (latitude and longitude). By addressing the limitations identified in the initial approach, this refined algorithm aims to improve detection accuracy and operational efficiency in complex rainforest environments. Tests in a selected area of the Amazon showed that the modified algorithm resulted in a recall drop of approximately 1% while reducing false positives by 26.6%. The recall drop means there was a decrease in the detection of true positives, which is balanced by the reduction in false positives. When applied across the entire biome, the recall decreased by 1.7%, but the total predictions dropped by 17.88%. These results suggest that, despite a slight reduction in recall, the modifications significantly improved the original algorithm by minimizing its limitations. Additionally, the improved solution demonstrates a 25.55% faster inference time, contributing to more rapid target identification. This advancement represents a meaningful step toward more effective detection of clandestine airstrips, supporting ongoing efforts to combat illegal activities in the region. Full article
(This article belongs to the Special Issue Visual Attributes in Computer Vision Applications)
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Review

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47 pages, 944 KiB  
Review
Algorithms for Plant Monitoring Applications: A Comprehensive Review
by Giovanni Paolo Colucci, Paola Battilani, Marco Camardo Leggieri and Daniele Trinchero
Algorithms 2025, 18(2), 84; https://doi.org/10.3390/a18020084 - 5 Feb 2025
Viewed by 1553
Abstract
Many sciences exploit algorithms in a large variety of applications. In agronomy, large amounts of agricultural data are handled by adopting procedures for optimization, clustering, or automatic learning. In this particular field, the number of scientific papers has significantly increased in recent years, [...] Read more.
Many sciences exploit algorithms in a large variety of applications. In agronomy, large amounts of agricultural data are handled by adopting procedures for optimization, clustering, or automatic learning. In this particular field, the number of scientific papers has significantly increased in recent years, triggered by scientists using artificial intelligence, comprising deep learning and machine learning methods or bots, to process field, crop, plant, or leaf images. Moreover, many other examples can be found, with different algorithms applied to plant diseases and phenology. This paper reviews the publications which have appeared in the past three years, analyzing the algorithms used and classifying the agronomic aims and the crops to which the methods are applied. Starting from a broad selection of 6060 papers, we subsequently refined the search, reducing the number to 358 research articles and 30 comprehensive reviews. By summarizing the advantages of applying algorithms to agronomic analyses, we propose a guide to farming practitioners, agronomists, researchers, and policymakers regarding best practices, challenges, and visions to counteract the effects of climate change, promoting a transition towards more sustainable, productive, and cost-effective farming and encouraging the introduction of smart technologies. Full article
(This article belongs to the Special Issue Visual Attributes in Computer Vision Applications)
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