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Algorithms
Special Issues
Modern Algorithms for Image Processing and Computer Vision
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Modern Algorithms for Image Processing and Computer Vision

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 502

Special Issue Editor

Dr. Arslan Munir

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
Interests: artificial intelligence; computer vision; parallel computing; embedded systems; secure and trustworthy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern image processing is a process of transforming an image into digital form and using computing systems to process, manipulate, and/or enhance digital images through various algorithms. Image processing is also a requisite for many computer vision tasks as it helps to preprocess images and prepare data in a form suitable for various computer vision models. Computer vision generally refers to techniques and algorithms that enable computers/machines to understand and make sense of images. Computer vision enables machines to extract latent information from visual data and to mimic the human perception of sight with computational algorithms. Active research is ongoing in relation to developing novel image processing and computer vision algorithms including deep learning-based algorithms for enabling new and fascinating applications.

This Special Issue targets algorithms for image processing and computer vision, inviting original research articles and reviews that relate to computing, architecture, algorithms, security, and applications of image processing and computer vision. Topics of interest include, but are not limited to, the following:

  • Image interpretation;
  • Object detection and recognition;
  • Spatial artificial intelligence;
  • Event detection and activity recognition;
  • Image segmentation;
  • Video classification and analysis;
  • Face and gesture recognition;
  • Pose estimation;
  • Computational photography;
  • Image security;
  • Vision hardware and/or software architectures;
  • Image/vision acceleration techniques;
  • Monitoring and surveillance;
  • Situational awareness.

Dr. Arslan Munir
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 1800 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
  • image fusion
  • vision algorithms
  • deep learning
  • stereo vision
  • activity recognition
  • image/video analysis
  • image encryption algorithms
  • computational photography
  • vision hardware/software
  • monitoring and surveillance
  • biometrics
  • robotics
  • augmented reality

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

Related Special Issue

Published Papers (1 paper)

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Research

16 pages, 1657 KiB  
Article
A Unified Framework for Recognizing Dynamic Hand Actions and Estimating Hand Pose from First-Person RGB Videos
by Jiayi Yang, Jiao Liang, Huimin Pan, Yuting Cai, Quanli Gao and Xihan Wang
Algorithms 2025, 18(7), 393; https://doi.org/10.3390/a18070393 - 27 Jun 2025
Viewed by 263
Abstract
Recognizing hand actions and poses from first-person RGB videos is crucial for applications like human–computer interaction. However, the recognition accuracy is often affected by factors such as occlusion and blurring. In this study, we propose a unified framework for action recognition and hand [...] Read more.
Recognizing hand actions and poses from first-person RGB videos is crucial for applications like human–computer interaction. However, the recognition accuracy is often affected by factors such as occlusion and blurring. In this study, we propose a unified framework for action recognition and hand pose estimation in first-person RGB videos. The framework consists of two main modules: the Hand Pose Estimation Module and the Action Recognition Module. In the Hand Pose Estimation Module, each video frame is fed into a multi-layer transformer encoder after passing through a feature extractor. The hand pose results and object categories for each frame are obtained through multi-layer perceptron prediction using a dual residual network structure. The above prediction results are concatenated with the feature information corresponding to each frame for subsequent action recognition tasks. In the Action Recognition Module, the feature vectors from each frame are aggregated by a multi-layer transformer encoder to capture the temporal information of the hand between video frames and obtain the motion trajectory. The final output is the category of hand movements in consecutive video frames. We conducted experiments on two publicly available datasets, FPHA and H2O, and the results show that our method achieves significant improvements on both datasets, with action recognition accuracies of 94.82% and 87.92%, respectively. Full article
(This article belongs to the Special Issue Modern Algorithms for Image Processing and Computer Vision)
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