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Computers
Special Issues
Advanced Image Processing and Computer Vision
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Advanced Image Processing and Computer Vision

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2066

Special Issue Editors

Dr. Selene Tomassini

E-Mail Website
Guest Editor
Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123 Povo, TN, Italy
Interests: advanced image processing; artificial intelligence; computer-aided detection and diagnosis; computer vision; decision-support systems; medical imaging
Dr. M. Ali Akber Dewan

E-Mail Website
Guest Editor
School of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Athabasca, AB T9S 3A3, Canada
Interests: image processing; computer vision; machine learning; pattern recognition, adaptive biometrics; artificial intelligence; medical image analysis

Special Issue Information

Dear Colleagues,

Advanced image processing (AIP) and computer vision (CV) are broad research topics, which continue to have impacts and generate innovation in a wide range of real-world applications. Nowadays, artificial intelligence (AI) permeates our daily activities and has become the core technology for AIP and CV tasks. Although these AI-based algorithms have achieved remarkable success, the existing technology has promising performance from a data-driven perspective. Thus, novel AI-based algorithms for AIP and CV are urgently needed, especially in those research fields in which data scarcity and/or data quality are concrete issues. Furthermore, the need for a human-centric AI approach should be highlighted, ensuring that the technology is ethical, explainable and fair.

This Special Issue aims to collect scientific articles, literature reviews and in-depth technical reports on the design, development and/or deployment of innovative AI-based algorithms for AIP and CV in real-world application scenarios, with a particular, though not sole, focus on medical scenarios.

Dr. Selene Tomassini
Dr. M. Ali Akber Dewan
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. Computers 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

  • advanced image processing
  • artificial intelligence
  • computational models
  • computer-aided detection and diagnosis
  • computer vision
  • decision-support systems
  • ethical artificial intelligence
  • explainable artificial intelligence
  • generative artificial intelligence
  • image analysis, interpretation and understanding
  • image-guided decision, planning and treatment
  • image pre- and post-processing
  • machine and deep learning
  • open-access image datasets
  • supervised, unsupervised and semi-supervised learning

Published Papers (2 papers)

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Research

13 pages, 762 KiB  
Article
Computer Vision Approach in Monitoring for Illicit and Copyrighted Objects in Digital Manufacturing
by Ihar Volkau, Sergei Krasovskii, Abdul Mujeeb and Helen Balinsky
Computers 2024, 13(4), 90; https://doi.org/10.3390/computers13040090 - 28 Mar 2024
Viewed by 576
Abstract
We propose a monitoring system for detecting illicit and copyrighted objects in digital manufacturing (DM). Our system is based on extracting and analyzing high-dimensional data from blueprints of three-dimensional (3D) objects. We aim to protect the legal interests of DM service providers, who [...] Read more.
We propose a monitoring system for detecting illicit and copyrighted objects in digital manufacturing (DM). Our system is based on extracting and analyzing high-dimensional data from blueprints of three-dimensional (3D) objects. We aim to protect the legal interests of DM service providers, who may receive requests for 3D printing from external sources, such as emails or uploads. Such requests may contain blueprints of objects that are illegal, restricted, or otherwise controlled in the country of operation or protected by copyright. Without a reliable way to identify such objects, the service provider may unknowingly violate the laws and regulations and face legal consequences. Therefore, we propose a multi-layer system that automatically detects and flags such objects before the 3D printing process begins. We present efficient computer vision algorithms for object analysis and scalable system architecture for data storage and processing and explain the rationale behind the suggested system architecture. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision)
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25 pages, 7135 KiB  
Article
A Seamless Deep Learning Approach for Apple Detection, Depth Estimation, and Tracking Using YOLO Models Enhanced by Multi-Head Attention Mechanism
by Praveen Kumar Sekharamantry, Farid Melgani, Jonni Malacarne, Riccardo Ricci, Rodrigo de Almeida Silva and Jose Marcato Junior
Computers 2024, 13(3), 83; https://doi.org/10.3390/computers13030083 - 21 Mar 2024
Viewed by 941
Abstract
Considering precision agriculture, recent technological developments have sparked the emergence of several new tools that can help to automate the agricultural process. For instance, accurately detecting and counting apples in orchards is essential for maximizing harvests and ensuring effective resource management. However, there [...] Read more.
Considering precision agriculture, recent technological developments have sparked the emergence of several new tools that can help to automate the agricultural process. For instance, accurately detecting and counting apples in orchards is essential for maximizing harvests and ensuring effective resource management. However, there are several intrinsic difficulties with traditional techniques for identifying and counting apples in orchards. To identify, recognize, and detect apples, apple target detection algorithms, such as YOLOv7, have shown a great deal of reflection and accuracy. But occlusions, electrical wiring, branches, and overlapping pose severe issues for precisely detecting apples. Thus, to overcome these issues and accurately recognize apples and find the depth of apples from drone-based videos in complicated backdrops, our proposed model combines a multi-head attention system with the YOLOv7 object identification framework. Furthermore, we provide the ByteTrack method for apple counting in real time, which guarantees effective monitoring of apples. To verify the efficacy of our suggested model, a thorough comparison assessment is performed with several current apple detection and counting techniques. The outcomes adequately proved the effectiveness of our strategy, which continuously surpassed competing methods to achieve exceptional accuracies of 0.92, 0.96, and 0.95 with respect to precision, recall, and F1 score, and a low MAPE of 0.027, respectively. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision)
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