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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 10806

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Dr. Samaneh Mazaheri

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Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada
Interests: artificial intelligence; computer vision; machine learning; deep learning; image processing; medical image processing; AI in healthcare

Special Issue Information

Dear Colleagues,

Computer vision adoption is growing steadily. Be it video-detection systems in self-driving cars, 3D printing in manufacturing, healthcare or advanced sensors in defense and logistics, technology is playing its part. Additionally, there is a growing call for integrating theoretical research on image processing algorithms with the more applied research on image processing systems.

This Special Issue is on topics of image processing and computer vision, along with their applications in different domains and systems in different environments. Recent advancements in image processing and computer vision have enabled researchers to boost the development of intelligent applications in different domains. These intelligent applications are integrated as real-world applications in the environment in order to collecting data seamlessly continuously and performing Image Processing on the huge amount of data collected from these environments. These intelligent applications are developed to be adaptable in different unexpected conditions, which makes these applications highly useful in real-world environments. There are numerous challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that can be applied on personal photos and videos.

This Special Issue aims to explore the variety of techniques used to analyze and interpret images, together with computer vision applications and advances, by providing a platform for researchers from both academia and industry to present their novel and unpublished work in the domains of computer vision and image processing. This will help to foster future research in the growing field of computer vision and its related areas.

Dr. Samaneh Mazaheri
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

Computer Vision:

3D computer vision
adversarial attacks and robustness
bias, fairness and privacy
biometrics, face, gesture and pose
computational photography, image and video synthesis
image and video retrieval
learning and optimization for computer vision
low level and physics-based vision
medical and biological imaging
motion and tracking
object detection and categorization
representation learning for vision
scene analysis and understanding
segmentation
video understanding and activity analysis
vision for robotics and autonomous driving
visual reasoning and symbolic representations
applications

Image Processing:

image sensing, modelling and representation
statistical modeling and estimation
image models (structure based, morphological, graph based)
image processing methods (linear and non-linear filtering, transforms, wavelets, etc.)
inverse imaging, compressive sensing
image acquisition, denoising, deblurring, reconstruction
machine learning and image processing algorithms
image segmentation and representation
image and video retrieval
image processing and understanding
knowledge representation and high-level vision
graph theoretic methods
animation, movies, advertising, video games
real-time image processing and learning
neural network applications
deep learning
human-centric self-supervised learning
machine vision
computer-generated imagery
biomedical image processing
biomedical and health informatics

Published Papers (12 papers)

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Research

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29 pages, 21511 KiB

Open AccessArticle

Enhancing Surveillance Vision with Multi-Layer Deep Learning Representation

by Dong-Min Son and Sung-Hak Lee

Mathematics 2024, 12(9), 1313; https://doi.org/10.3390/math12091313 - 25 Apr 2024

Viewed by 181

Abstract

This paper aimed to develop a method for generating sand–dust removal and dehazed images utilizing CycleGAN, facilitating object identification on roads under adverse weather conditions such as heavy dust or haze, which severely impair visibility. Initially, the study addressed the scarcity of paired image sets for training by employing unpaired CycleGAN training. The CycleGAN training module incorporates hierarchical single-scale Retinex (SSR) images with varying sigma sizes, facilitating multiple-scaled trainings. Refining the training data into detailed hierarchical layers for virtual paired training enhances the performance of CycleGAN training. Conventional sand–dust removal or dehazing algorithms, alongside deep learning methods, encounter challenges in simultaneously addressing sand–dust removal and dehazing with a singular algorithm. Such algorithms often necessitate resetting hyperparameters to process images from both scenarios. To overcome this limitation, we proposed a unified approach for removing sand–dust and haze phenomena using a single model, leveraging images processed hierarchically with SSR. The image quality and image sharpness metrics of the proposed method were BRIQUE, PIQE, CEIQ, MCMA, LPC-SI, and S3. In sand–dust environments, the proposed method achieved the highest scores, with an average of 21.52 in BRISQUE, 0.724 in MCMA, and 0.968 in LPC-SI compared to conventional methods. For haze images, the proposed method outperformed conventional methods with an average of 3.458 in CEIQ, 0.967 in LPC-SI, and 0.243 in S3. The images generated via this proposed method demonstrated superior performance in image quality and sharpness evaluation compared to conventional algorithms. The outcomes of this study hold particular relevance for camera images utilized in automobiles, especially in the context of self-driving cars or CCTV surveillance systems. Full article

(This article belongs to the Special Issue New Advances and Applications in Image Processing and Computer Vision)

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22 pages, 3459 KiB

Open AccessArticle

MDER-Net: A Multi-Scale Detail-Enhanced Reverse Attention Network for Semantic Segmentation of Bladder Tumors in Cystoscopy Images

by Chao Nie, Chao Xu and Zhengping Li

Mathematics 2024, 12(9), 1281; https://doi.org/10.3390/math12091281 - 24 Apr 2024

Viewed by 253

Abstract

White light cystoscopy is the gold standard for the diagnosis of bladder cancer. Automatic and accurate tumor detection is essential to improve the surgical resection of bladder cancer and reduce tumor recurrence. At present, Transformer-based medical image segmentation algorithms face challenges in restoring fine-grained detail information and local boundary information of features and have limited adaptability to multi-scale features of lesions. To address these issues, we propose a new multi-scale detail-enhanced reverse attention network, MDER-Net, for accurate and robust bladder tumor segmentation. Firstly, we propose a new multi-scale efficient channel attention module (MECA) to process four different levels of features extracted by the PVT v2 encoder to adapt to the multi-scale changes in bladder tumors; secondly, we use the dense aggregation module (DA) to aggregate multi-scale advanced semantic feature information; then, the similarity aggregation module (SAM) is used to fuse multi-scale high-level and low-level features, complementing each other in position and detail information; finally, we propose a new detail-enhanced reverse attention module (DERA) to capture non-salient boundary features and gradually explore supplementing tumor boundary feature information and fine-grained detail information; in addition, we propose a new efficient channel space attention module (ECSA) that enhances local context and improves segmentation performance by suppressing redundant information in low-level features. Extensive experiments on the bladder tumor dataset BtAMU, established in this article, and five publicly available polyp datasets show that MDER-Net outperforms eight state-of-the-art (SOTA) methods in terms of effectiveness, robustness, and generalization ability. Full article

(This article belongs to the Special Issue New Advances and Applications in Image Processing and Computer Vision)

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10 pages, 318 KiB

Open AccessArticle

Tiling Rectangles and the Plane Using Squares of Integral Sides

by Bahram Sadeghi Bigham, Mansoor Davoodi Monfared, Samaneh Mazaheri and Jalal Kheyrabadi

Mathematics 2024, 12(7), 1027; https://doi.org/10.3390/math12071027 - 29 Mar 2024

Viewed by 375

Abstract

We study the problem of perfect tiling in the plane and explore the possibility of tiling a rectangle using integral distinct squares. Assume a set of distinguishable squares (or equivalently a set of distinct natural numbers) is given, and one has to decide whether it can tile the plane or a rectangle or not. Previously, it has been proved that tiling the plane is not feasible using a set of odd numbers or an infinite sequence of natural numbers including exactly two odd numbers. The problem is open for different situations in which the number of odd numbers is arbitrary. In addition to providing a solution to this special case, we discuss some open problems to tile the plane and rectangles in this paper. Full article

(This article belongs to the Special Issue New Advances and Applications in Image Processing and Computer Vision)

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27 pages, 5593 KiB

Open AccessArticle

Ear-Touch-Based Mobile User Authentication

by Jalil Nourmohammadi Khiarak, Samaneh Mazaheri and Rohollah Moosavi Tayebi

Mathematics 2024, 12(5), 752; https://doi.org/10.3390/math12050752 - 02 Mar 2024

Viewed by 534

Abstract

Mobile devices have become integral to daily life, necessitating robust user authentication methods to safeguard personal information. In this study, we present a new approach to mobile user authentication utilizing ear-touch interactions. Our novel system employs an analytical algorithm to authenticate users based on features extracted from ear-touch images. We conducted extensive evaluations on a dataset comprising ear-touch images from 92 subjects, achieving an average equal error rate of 0.04, indicative of high accuracy and reliability. Our results suggest that ear-touch-based authentication is a feasible and effective method for securing mobile devices. Full article

(This article belongs to the Special Issue New Advances and Applications in Image Processing and Computer Vision)

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19 pages, 9187 KiB

Open AccessArticle

Light “You Only Look Once”: An Improved Lightweight Vehicle-Detection Model for Intelligent Vehicles under Dark Conditions

by Tianrui Yin, Wei Chen, Bo Liu, Changzhen Li and Luyao Du

Mathematics 2024, 12(1), 124; https://doi.org/10.3390/math12010124 - 29 Dec 2023

Viewed by 832

Abstract

Vehicle detection is crucial for traffic surveillance and assisted driving. To overcome the loss of efficiency, accuracy, and stability in low-light conditions, we propose a lightweight “You Only Look Once” (YOLO) detection model. A polarized self-attention-enhanced aggregation feature pyramid network is used to improve feature extraction and fusion in low-light scenarios, and enhanced “Swift” spatial pyramid pooling is used to reduce model parameters and enhance real-time nighttime detection. To address imbalanced low-light samples, we integrate an anchor mechanism with a focal loss to improve network stability and accuracy. Ablation experiments show the superior accuracy and real-time performance of our Light-YOLO model. Compared with EfficientNetv2-YOLOv5, Light-YOLO boosts mAP@0.5 and mAP@0.5:0.95 by 4.03 and 2.36%, respectively, cuts parameters by 44.37%, and increases recognition speed by 20.42%. Light-YOLO competes effectively with advanced lightweight networks and offers a solution for efficient nighttime vehicle-detection. Full article

(This article belongs to the Special Issue New Advances and Applications in Image Processing and Computer Vision)

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16 pages, 7068 KiB

Open AccessArticle

Enhancing Focus Volume through Perceptual Focus Factor in Shape-from-Focus

by Khurram Ashfaq and Muhammad Tariq Mahmood

Mathematics 2024, 12(1), 102; https://doi.org/10.3390/math12010102 - 27 Dec 2023

Viewed by 668

Abstract

Shape From Focus (SFF) reconstructs a scene’s shape using a series of images with varied focus settings. However, the effectiveness of SFF largely depends on the Focus Measure (FM) used, which is prone to noise-induced inaccuracies in focus values. To address these issues, we introduce a perception-influenced factor to refine the traditional Focus Volume (FV) derived from a traditional FM. Owing to the strong relationship between the Difference of Gaussians (DoG) and how the visual system perceives edges in a scene, we apply it to local areas of the image sequence by segmenting the image sequence into non-overlapping blocks. This process yields a new metric, the Perceptual Focus Factor (PFF), which we combine with the traditional FV to obtain an enhanced FV and, ultimately, an enhanced depth map. Intensive experiments are conducted by using fourteen synthetic and six real-world data sets. The performance of the proposed method is evaluated using quantitative measures, such as Root Mean Square Error (RMSE) and correlation. For fourteen synthetic data sets, the average RMSE measure of 6.88 and correction measure of 0.65 are obtained, which are improved through PFF from an RMSE of 7.44 and correlation of 0.56, respectively. Experimental results and comparative analysis demonstrate that the proposed approach outperforms the traditional state-of-the-art FMs in extracting depth maps. Full article

(This article belongs to the Special Issue New Advances and Applications in Image Processing and Computer Vision)

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34 pages, 9382 KiB

Open AccessArticle

LRFID-Net: A Local-Region-Based Fake-Iris Detection Network for Fake Iris Images Synthesized by a Generative Adversarial Network

by Jung Soo Kim, Young Won Lee, Jin Seong Hong, Seung Gu Kim, Ganbayar Batchuluun and Kang Ryoung Park

Mathematics 2023, 11(19), 4160; https://doi.org/10.3390/math11194160 - 03 Oct 2023

Viewed by 1049

Abstract

Iris recognition is a biometric method using the pattern of the iris seated between the pupil and the sclera for recognizing people. It is widely applied in various fields owing to its high accuracy in recognition and high security. A spoof detection method for discriminating a spoof attack is essential in biometric recognition systems that include iris recognition. However, previous studies have mainly investigated spoofing attack detection methods based on printed or photographed images, video replaying, artificial eyes, and patterned contact lenses fabricated using iris images from information leakage. On the other hand, there have only been a few studies on spoof attack detection using iris images generated through a generative adversarial network (GAN), which is a method that has drawn considerable research interest with the recent development of deep learning, and the enhancement of spoof detection accuracy by the methods proposed in previous research is limited. To address this problem, the possibility of an attack on a conventional iris recognition system with spoofed iris images generated using cycle-consistent adversarial networks (CycleGAN), which was the motivation of this study, was investigated. In addition, a local region-based fake-iris detection network (LRFID-Net) was developed. It provides a novel method for discriminating fake iris images by segmenting the iris image into three regions based on the iris region. Experimental results using two open databases, the Warsaw LiveDet-Iris-2017 and the Notre Dame Contact Lens Detection LiveDet-Iris-2017 datasets, showed that the average classification error rate of spoof detection by the proposed method was 0.03% for the Warsaw dataset and 0.11% for the Notre Dame Contact Lens Detection dataset. The results confirmed that the proposed method outperformed the state-of-the-art methods. Full article

(This article belongs to the Special Issue New Advances and Applications in Image Processing and Computer Vision)

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21 pages, 6458 KiB

Open AccessArticle

CAGNet: A Multi-Scale Convolutional Attention Method for Glass Detection Based on Transformer

by Xiaohang Hu, Rui Gao, Seungjun Yang and Kyungeun Cho

Mathematics 2023, 11(19), 4084; https://doi.org/10.3390/math11194084 - 26 Sep 2023

Viewed by 644

Abstract

Glass plays a vital role in several fields, making its accurate detection crucial. Proper detection prevents misjudgments, reduces noise from reflections, and ensures optimal performance in other computer vision tasks. However, the prevalent usage of glass in daily applications poses unique challenges for computer vision. This study introduces a novel convolutional attention glass segmentation network (CAGNet) predicated on a transformer architecture customized for image glass detection. Based on the foundation of our prior study, CAGNet minimizes the number of training cycles and iterations, resulting in enhanced performance and efficiency. CAGNet is built upon the strategic design and integration of two types of convolutional attention mechanisms coupled with a transformer head applied for comprehensive feature analysis and fusion. To further augment segmentation precision, the network incorporates a custom edge-weighting scheme to optimize glass detection within images. Comparative studies and rigorous testing demonstrate that CAGNet outperforms several leading methodologies in glass detection, exhibiting robustness across a diverse range of conditions. Specifically, the IOU metric improves by 0.26% compared to that in our previous study and presents a 0.92% enhancement over those of other state-of-the-art methods. Full article

(This article belongs to the Special Issue New Advances and Applications in Image Processing and Computer Vision)

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21 pages, 44259 KiB

Open AccessArticle

Cervical Precancerous Lesion Image Enhancement Based on Retinex and Histogram Equalization

by Yuan Ren, Zhengping Li and Chao Xu

Mathematics 2023, 11(17), 3689; https://doi.org/10.3390/math11173689 - 28 Aug 2023

Viewed by 1509

Abstract

Cervical cancer is a prevalent chronic malignant tumor in gynecology, necessitating high-quality images of cervical precancerous lesions to enhance detection rates. Addressing the challenges of low contrast, uneven illumination, and indistinct lesion details in such images, this paper proposes an enhancement algorithm based on retinex and histogram equalization. First, the algorithm solves the color deviation problem by modifying the quantization formula of retinex theory. Then, the contrast-limited adaptive histogram equalization algorithm is selectively conducted on blue and green channels to avoid the problem of image visual quality reduction caused by drastic darkening of local dark areas. Next, a multi-scale detail enhancement algorithm is used to further sharpen the details. Finally, the problem of noise amplification and image distortion in the process of enhancement is alleviated by dynamic weighted fusion. The experimental results confirm the effectiveness of the proposed algorithm in optimizing brightness, enhancing contrast, sharpening details, and suppressing noise in cervical precancerous lesion images. The proposed algorithm has shown superior performance compared to other traditional methods based on objective indicators such as peak signal-to-noise ratio, detail-variance–background-variance, gray square mean deviation, contrast improvement index, and enhancement quality index. Full article

(This article belongs to the Special Issue New Advances and Applications in Image Processing and Computer Vision)

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12 pages, 20257 KiB

Open AccessArticle

Directional Ring Difference Filter for Robust Shape-from-Focus

by Khurram Ashfaq and Muhammad Tariq Mahmood

Mathematics 2023, 11(14), 3056; https://doi.org/10.3390/math11143056 - 11 Jul 2023

Cited by 1 | Viewed by 894

Abstract

In the shape-from-focus (SFF) method, the quality of the 3D shape generated relies heavily on the focus measure operator (FM) used. Unfortunately, most FMs are sensitive to noise and provide inaccurate depth maps. Among recent FMs, the ring difference filter (RDF) has demonstrated excellent robustness against noise and reasonable performance in computing accurate depth maps. However, it also suffers from the response cancellation problem (RCP) encountered in multidimensional kernel-based FMs. To address this issue, we propose an effective and robust FM called the directional ring difference filter (DRDF). In DRDF, the focus quality is computed by aggregating responses of RDF from multiple kernels in different directions. We conducted experiments using synthetic and real image datasets and found that the proposed DRDF method outperforms traditional FMs in terms of noise handling and producing a higher quality 3D shape estimate of the object. Full article

(This article belongs to the Special Issue New Advances and Applications in Image Processing and Computer Vision)

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15 pages, 1329 KiB

Open AccessArticle

Optimal Robot Pose Estimation Using Scan Matching by Turning Function

by Bahram Sadeghi Bigham, Omid Abbaszadeh, Mazyar Zahedi-Seresht, Shahrzad Khosravi and Elham Zarezadeh

Mathematics 2023, 11(6), 1449; https://doi.org/10.3390/math11061449 - 16 Mar 2023

Viewed by 1294

Abstract

The turning function is a tool in image processing that measures the difference between two polygonal shapes. We propose a localization algorithm for the optimal pose estimation of autonomous mobile robots using the scan-matching method based on the turning function algorithm. There are several methodologies aimed at moving the robots in the right way and carrying out their missions well, which involves the integration of localization and control. In the proposed method, the localization problem is implemented in the form of an optimization problem. Afterwards, the turning function algorithm and the simplex method are applied to estimate the localization and orientation of the robot. The proposed algorithm first receives the polygons extracted from two sensors’ data and then allocates a histogram to each sensor scan. This algorithm attempts to maximize the similarity of the two histograms by converting them to a unified coordinate system. In this way, the estimate of the difference between the two situations is calculated. In more detail, the main objective of this study is to provide an algorithm aimed at reducing errors in the localization and orientation of mobile robots. The simulation results indicate the great performance of this algorithm. Experimental results on simulated and real datasets show that the proposed algorithms achieve better results in terms of both position and orientation metrics. Full article

(This article belongs to the Special Issue New Advances and Applications in Image Processing and Computer Vision)

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Review

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26 pages, 5886 KiB

Open AccessReview

Progress in Blind Image Quality Assessment: A Brief Review

by Pei Yang, Jordan Sturtz and Letu Qingge

Mathematics 2023, 11(12), 2766; https://doi.org/10.3390/math11122766 - 19 Jun 2023

Cited by 2 | Viewed by 1540

Abstract

As a fundamental research problem, blind image quality assessment (BIQA) has attracted increasing interest in recent years. Although great progress has been made, BIQA still remains a challenge. To better understand the research progress and challenges in this field, we review BIQA methods in this paper. First, we introduce the BIQA problem definition and related methods. Second, we provide a detailed review of the existing BIQA methods in terms of representative hand-crafted features, learning-based features and quality regressors for two-stage methods, as well as one-stage DNN models with various architectures. Moreover, we also present and analyze the performance of competing BIQA methods on six public IQA datasets. Finally, we conclude our paper with possible future research directions based on a performance analysis of the BIQA methods. This review will provide valuable references for researchers interested in the BIQA problem. Full article

(This article belongs to the Special Issue New Advances and Applications in Image Processing and Computer Vision)

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