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Computers
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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: 31 December 2024 | Viewed by 14848

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

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Published Papers (7 papers)

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Research

Jump to: Review

19 pages, 37808 KiB  
Article
Modified Multiresolution Convolutional Neural Network for Quasi-Periodic Noise Reduction in Phase Shifting Profilometry for 3D Reconstruction
by Osmar Antonio Espinosa-Bernal, Jesús Carlos Pedraza-Ortega, Marco Antonio Aceves-Fernandez, Juan Manuel Ramos-Arreguín, Saul Tovar-Arriaga and Efrén Gorrostieta-Hurtado
Computers 2024, 13(11), 290; https://doi.org/10.3390/computers13110290 - 8 Nov 2024
Viewed by 375
Abstract
Fringe profilometry is a method that obtains the 3D information of objects by projecting a pattern of fringes. The three-step technique uses only three images to acquire the 3D information from an object, and many studies have been conducted to improve this technique. [...] Read more.
Fringe profilometry is a method that obtains the 3D information of objects by projecting a pattern of fringes. The three-step technique uses only three images to acquire the 3D information from an object, and many studies have been conducted to improve this technique. However, there is a problem that is inherent to this technique, and that is the quasi-periodic noise that appears due to this technique and considerably affects the final 3D object reconstructed. Many studies have been carried out to tackle this problem to obtain a 3D object close to the original one. The application of deep learning in many areas of research presents a great opportunity to to reduce or eliminate the quasi-periodic noise that affects images. Therefore, a model of convolutional neural network along with four different patterns of frequencies projected in the three-step technique is researched in this work. The inferences produced by models trained with different frequencies are compared with the original ones both qualitatively and quantitatively. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision)
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24 pages, 4449 KiB  
Article
Empowering Communication: A Deep Learning Framework for Arabic Sign Language Recognition with an Attention Mechanism
by R. S. Abdul Ameer, M. A. Ahmed, Z. T. Al-Qaysi, M. M. Salih and Moceheb Lazam Shuwandy
Computers 2024, 13(6), 153; https://doi.org/10.3390/computers13060153 - 19 Jun 2024
Cited by 2 | Viewed by 1327
Abstract
This article emphasises the urgent need for appropriate communication tools for communities of people who are deaf or hard-of-hearing, with a specific emphasis on Arabic Sign Language (ArSL). In this study, we use long short-term memory (LSTM) models in conjunction with MediaPipe to [...] Read more.
This article emphasises the urgent need for appropriate communication tools for communities of people who are deaf or hard-of-hearing, with a specific emphasis on Arabic Sign Language (ArSL). In this study, we use long short-term memory (LSTM) models in conjunction with MediaPipe to reduce the barriers to effective communication and social integration for deaf communities. The model design incorporates LSTM units and an attention mechanism to handle the input sequences of extracted keypoints from recorded gestures. The attention layer selectively directs its focus toward relevant segments of the input sequence, whereas the LSTM layer handles temporal relationships and encodes the sequential data. A comprehensive dataset comprised of fifty frequently used words and numbers in ArSL was collected for developing the recognition model. This dataset comprises many instances of gestures recorded by five volunteers. The results of the experiment support the effectiveness of the proposed approach, as the model achieved accuracies of more than 85% (individual volunteers) and 83% (combined data). The high level of precision emphasises the potential of artificial intelligence-powered translation software to improve effective communication for people with hearing impairments and to enable them to interact with the larger community more easily. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision)
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22 pages, 5829 KiB  
Article
Enhancing Brain Segmentation in MRI through Integration of Hidden Markov Random Field Model and Whale Optimization Algorithm
by Abdelaziz Daoudi and Saïd Mahmoudi
Computers 2024, 13(5), 124; https://doi.org/10.3390/computers13050124 - 17 May 2024
Viewed by 975
Abstract
The automatic delineation and segmentation of the brain tissues from Magnetic Resonance Images (MRIs) is a great challenge in the medical context. The difficulty of this task arises out of the similar visual appearance of neighboring brain structures in MR images. In this [...] Read more.
The automatic delineation and segmentation of the brain tissues from Magnetic Resonance Images (MRIs) is a great challenge in the medical context. The difficulty of this task arises out of the similar visual appearance of neighboring brain structures in MR images. In this study, we present an automatic approach for robust and accurate brain tissue boundary outlining in MR images. This algorithm is proposed for the tissue classification of MR brain images into White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). The proposed segmentation process combines two algorithms, the Hidden Markov Random Field (HMRF) model and the Whale Optimization Algorithm (WOA), to enhance the treatment accuracy. In addition, we use the Whale Optimization Algorithm (WOA) to optimize the performance of the segmentation method. The experimental results from a dataset of brain MR images show the superiority of our proposed method, referred to HMRF-WOA, as compared to other reported approaches. The HMRF-WOA is evaluated on multiple MRI contrasts, including both simulated and real MR brain images. The well-known Dice coefficient (DC) and Jaccard coefficient (JC) were used as similarity metrics. The results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice coefficient and Jaccard coefficient above 0.9. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision)
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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 1231
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
Cited by 4 | Viewed by 2359
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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Review

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22 pages, 4930 KiB  
Review
Quantum Image Compression: Fundamentals, Algorithms, and Advances
by Sowmik Kanti Deb and W. David Pan
Computers 2024, 13(8), 185; https://doi.org/10.3390/computers13080185 - 25 Jul 2024
Viewed by 1421
Abstract
Quantum computing has emerged as a transformative paradigm, with revolutionary potential in numerous fields, including quantum image processing and compression. Applications that depend on large scale image data could benefit greatly from parallelism and quantum entanglement, which would allow images to be encoded [...] Read more.
Quantum computing has emerged as a transformative paradigm, with revolutionary potential in numerous fields, including quantum image processing and compression. Applications that depend on large scale image data could benefit greatly from parallelism and quantum entanglement, which would allow images to be encoded and decoded with unprecedented efficiency and data reduction capability. This paper provides a comprehensive overview of the rapidly evolving field of quantum image compression, including its foundational principles, methods, challenges, and potential uses. The paper will also feature a thorough exploration of the fundamental concepts of quantum qubits as image pixels, quantum gates as image transformation tools, quantum image representation, as well as basic quantum compression operations. Our survey shows that work is still sparse on the practical implementation of quantum image compression algorithms on physical quantum computers. Thus, further research is needed in order to attain the full advantage and potential of quantum image compression algorithms on large-scale fault-tolerant quantum computers. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision)
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44 pages, 4162 KiB  
Review
Object Tracking Using Computer Vision: A Review
by Pushkar Kadam, Gu Fang and Ju Jia Zou
Computers 2024, 13(6), 136; https://doi.org/10.3390/computers13060136 - 28 May 2024
Cited by 2 | Viewed by 5835
Abstract
Object tracking is one of the most important problems in computer vision applications such as robotics, autonomous driving, and pedestrian movement. There has been a significant development in camera hardware where researchers are experimenting with the fusion of different sensors and developing image [...] Read more.
Object tracking is one of the most important problems in computer vision applications such as robotics, autonomous driving, and pedestrian movement. There has been a significant development in camera hardware where researchers are experimenting with the fusion of different sensors and developing image processing algorithms to track objects. Image processing and deep learning methods have significantly progressed in the last few decades. Different data association methods accompanied by image processing and deep learning are becoming crucial in object tracking tasks. The data requirement for deep learning methods has led to different public datasets that allow researchers to benchmark their methods. While there has been an improvement in object tracking methods, technology, and the availability of annotated object tracking datasets, there is still scope for improvement. This review contributes by systemically identifying different sensor equipment, datasets, methods, and applications, providing a taxonomy about the literature and the strengths and limitations of different approaches, thereby providing guidelines for selecting equipment, methods, and applications. Research questions and future scope to address the unresolved issues in the object tracking field are also presented with research direction guidelines. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Number Recognition through Color Distortion using Convolutional Neural Networks
Author: Henshaw
Highlights: The paper captures new image processing techniques applied to character sets that emulate colorblindness.

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