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J. Imaging
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Feature Papers in Section AI in Imaging
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Feature Papers in Section AI in Imaging

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "AI in Imaging".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 8347

Special Issue Editor

Prof. Dr. Antonio Fernández-Caballero

E-Mail Website
Guest Editor
Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
Interests: pattern recognition; human–computer interaction; affective computing; computer vision; multi-sensor fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to launch a Special Issue on “AI in Imaging”. Artificial Intelligence (AI) techniques are being used by the imaging academia and industry to solve a wide range of previously intractable problems. Image recognition and understanding are considered important subfields of AI. In addition, topics that are at the core of AI, such as machine learning, knowledge engineering, reasoning and inference, are common to imaging researchers. Therefore, this Special Issue provides a forum for the publication of articles describing the use of classical and modern AI methods in imaging applications.

This Special Issue aims to provide a collection of high-quality research articles that address broad challenges on both theoretical and application aspects of AI in Imaging. We invite colleagues to contribute original research articles as well as review articles that will stimulate the continuing effort on the application of AI approaches to solve imaging problems.

The topics of this Special Issue on “AI in Imaging” explicitly include (but are not limited to) the following aspects:

  • Machine learning in imaging;
  • Expert systems in imaging;
  • Knowledge engineering in imaging;
  • Neural networks in imaging;
  • Intelligent agents and multiagent systems in imaging;
  • Evolutionary and fuzzy computation in imaging;
  • Reasoning and inference in imaging;
  • Applications of artificial intelligence in imaging.

Prof. Dr. Antonio Fernández-Caballero
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. Journal of Imaging 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.

Published Papers (5 papers)

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Research

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14 pages, 605 KiB  
Article
Fully Self-Supervised Out-of-Domain Few-Shot Learning with Masked Autoencoders
by Reece Walsh, Islam Osman, Omar Abdelaziz and Mohamed S. Shehata
J. Imaging 2024, 10(1), 23; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging10010023 - 16 Jan 2024
Viewed by 1533
Abstract
Few-shot learning aims to identify unseen classes with limited labelled data. Recent few-shot learning techniques have shown success in generalizing to unseen classes; however, the performance of these techniques has also been shown to degrade when tested on an out-of-domain setting. Previous work, [...] Read more.
Few-shot learning aims to identify unseen classes with limited labelled data. Recent few-shot learning techniques have shown success in generalizing to unseen classes; however, the performance of these techniques has also been shown to degrade when tested on an out-of-domain setting. Previous work, additionally, has also demonstrated increasing reliance on supervised finetuning in an off-line or online capacity. This paper proposes a novel, fully self-supervised few-shot learning technique (FSS) that utilizes a vision transformer and masked autoencoder. The proposed technique can generalize to out-of-domain classes by finetuning the model in a fully self-supervised method for each episode. We evaluate the proposed technique using three datasets (all out-of-domain). As such, our results show that FSS has an accuracy gain of 1.05%, 0.12%, and 1.28% on the ISIC, EuroSat, and BCCD datasets, respectively, without the use of supervised training. Full article
(This article belongs to the Special Issue Feature Papers in Section AI in Imaging)
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39 pages, 922 KiB  
Article
Constraints on Optimising Encoder-Only Transformers for Modelling Sign Language with Human Pose Estimation Keypoint Data
by Luke T. Woods and Zeeshan A. Rana
J. Imaging 2023, 9(11), 238; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging9110238 - 02 Nov 2023
Cited by 1 | Viewed by 1343
Abstract
Supervised deep learning models can be optimised by applying regularisation techniques to reduce overfitting, which can prove difficult when fine tuning the associated hyperparameters. Not all hyperparameters are equal, and understanding the effect each hyperparameter and regularisation technique has on the performance of [...] Read more.
Supervised deep learning models can be optimised by applying regularisation techniques to reduce overfitting, which can prove difficult when fine tuning the associated hyperparameters. Not all hyperparameters are equal, and understanding the effect each hyperparameter and regularisation technique has on the performance of a given model is of paramount importance in research. We present the first comprehensive, large-scale ablation study for an encoder-only transformer to model sign language using the improved Word-level American Sign Language dataset (WLASL-alt) and human pose estimation keypoint data, with a view to put constraints on the potential to optimise the task. We measure the impact a range of model parameter regularisation and data augmentation techniques have on sign classification accuracy. We demonstrate that within the quoted uncertainties, other than ℓ2 parameter regularisation, none of the regularisation techniques we employ have an appreciable positive impact on performance, which we find to be in contradiction to results reported by other similar, albeit smaller scale, studies. We also demonstrate that the model architecture is bounded by the small dataset size for this task over finding an appropriate set of model parameter regularisation and common or basic dataset augmentation techniques. Furthermore, using the base model configuration, we report a new maximum top-1 classification accuracy of 84% on 100 signs, thereby improving on the previous benchmark result for this model architecture and dataset. Full article
(This article belongs to the Special Issue Feature Papers in Section AI in Imaging)
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19 pages, 4088 KiB  
Article
Threshold-Based BRISQUE-Assisted Deep Learning for Enhancing Crack Detection in Concrete Structures
by Sanjeetha Pennada, Marcus Perry, Jack McAlorum, Hamish Dow and Gordon Dobie
J. Imaging 2023, 9(10), 218; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging9100218 - 10 Oct 2023
Cited by 2 | Viewed by 1554
Abstract
Automated visual inspection has made significant advancements in the detection of cracks on the surfaces of concrete structures. However, low-quality images significantly affect the classification performance of convolutional neural networks (CNNs). Therefore, it is essential to evaluate the suitability of image datasets used [...] Read more.
Automated visual inspection has made significant advancements in the detection of cracks on the surfaces of concrete structures. However, low-quality images significantly affect the classification performance of convolutional neural networks (CNNs). Therefore, it is essential to evaluate the suitability of image datasets used in deep learning models, like Visual Geometry Group 16 (VGG16), for accurate crack detection. This study explores the sensitivity of the BRISQUE method to different types of image degradations, such as Gaussian noise and Gaussian blur. By evaluating the performance of the VGG16 model on these degraded datasets with varying levels of noise and blur, a correlation is established between image degradation and BRISQUE scores. The results demonstrate that images with lower BRISQUE scores achieve higher accuracy, F1 score, and Matthew’s correlation coefficient (MCC) in crack classification. The study proposes the implementation of a BRISQUE score threshold (BT) to optimise training and testing times, leading to reduced computational costs. These findings have significant implications for enhancing accuracy and reliability in automated visual inspection systems for crack detection and structural health monitoring (SHM). Full article
(This article belongs to the Special Issue Feature Papers in Section AI in Imaging)
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17 pages, 1740 KiB  
Article
Data-Weighted Multivariate Generalized Gaussian Mixture Model: Application to Point Cloud Robust Registration
by Bingwei Ge, Fatma Najar and Nizar Bouguila
J. Imaging 2023, 9(9), 179; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging9090179 - 31 Aug 2023
Viewed by 1349
Abstract
In this paper, a weighted multivariate generalized Gaussian mixture model combined with stochastic optimization is proposed for point cloud registration. The mixture model parameters of the target scene and the scene to be registered are updated iteratively by the fixed point method under [...] Read more.
In this paper, a weighted multivariate generalized Gaussian mixture model combined with stochastic optimization is proposed for point cloud registration. The mixture model parameters of the target scene and the scene to be registered are updated iteratively by the fixed point method under the framework of the EM algorithm, and the number of components is determined based on the minimum message length criterion (MML). The KL divergence between these two mixture models is utilized as the loss function for stochastic optimization to find the optimal parameters of the transformation model. The self-built point clouds are used to evaluate the performance of the proposed algorithm on rigid registration. Experiments demonstrate that the algorithm dramatically reduces the impact of noise and outliers and effectively extracts the key features of the data-intensive regions. Full article
(This article belongs to the Special Issue Feature Papers in Section AI in Imaging)
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Other

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29 pages, 971 KiB  
Systematic Review
A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection
by Esteban Cumbajin, Nuno Rodrigues, Paulo Costa, Rolando Miragaia, Luís Frazão, Nuno Costa, Antonio Fernández-Caballero, Jorge Carneiro, Leire H. Buruberri and António Pereira
J. Imaging 2023, 9(10), 193; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging9100193 - 25 Sep 2023
Cited by 5 | Viewed by 2116
Abstract
Surface defect detection with machine learning has become an important tool in industries and a large field of study for researchers or workers in recent years. It is necessary to have a simplified source of information that helps us to better focus on [...] Read more.
Surface defect detection with machine learning has become an important tool in industries and a large field of study for researchers or workers in recent years. It is necessary to have a simplified source of information that helps us to better focus on one type of surface. In this systematic review, we present a classification for surface defect detection based on convolutional neural networks (CNNs) focused on surface types. Findings: Out of 253 records identified, 59 primary studies were eligible. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed the structures of each study and the concepts related to defects and their types on surfaces. The presented review is mainly focused on finding a classification for the types of surfaces most used in industry (metal, building, ceramic, wood, and special). We delve into the specifics of each surface category, offering illustrative examples of their applications within both industrial and laboratory settings. Furthermore, we propose a new taxonomy of machine learning based on the obtained results and collected information. We summarized the studies and extracted the main characteristics such as type of surface, problem types, timeline, type of network, techniques, and datasets. Among the most relevant results of our analysis, we found that the metallic surface is the most used, as it is the one found in 62.71% of the studies, and the most prevalent problem type is classification, accounting for 49.15% of the total. Furthermore, we observe that transfer learning was employed in 83.05% of the studies, while data augmentation was utilized in 59.32%. Our findings also provide insights into the cameras most frequently employed, along with the strategies adopted to address illumination challenges present in certain articles and the approach to creating datasets for real-world applications. The main results presented in this review allow for a quick and efficient search of information for researchers and professionals interested in improving the results of their defect detection projects. Finally, we analyzed the trends that could open new fields of study for future research in the area of surface defect detection. Full article
(This article belongs to the Special Issue Feature Papers in Section AI in Imaging)
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